Measuring the joint effect of sony and erricson
阿尔法系数和合成信度的关系

RESEARCH REPORTOn the Relationship Between Coefficient Alpha and Composite ReliabilityRobert A.Peterson and Yeolib KimThe University of Texas at AustinCronbach’s coefficient alpha is the most widely used estimator of the reliability of tests and scales.However,it has been criticized as being a lower bound and hence underestimating true reliability.A popular alternative to coefficient alpha is composite reliability,which is usually calculated in conjunction with structural equation modeling.A quantitative analysis of 2,524pairs of coefficient alpha and composite reliability values derived from empirical investigations revealed that although the average composite reliability value (.86)exceeded the average corresponding coefficient alpha value (.84),the difference was relatively inconsequential for practical applications such as meta-analysis.Keywords:coefficient alpha,composite reliability,meta-analysisSix decades ago,Cronbach’s (1951)seminal article,“Coeffi-cient Alpha and the Internal Structure of Tests,”was published in Psychometrika .This article changed the manner in which test and scale reliability was conceptualized and measured.Coefficient alpha has since become a standard component of the toolkits of researchers attempting to measure reliability,and the article has been cited nearly 17,000times in GoogleScholar.It is,without question,the most widely used estimator of test and scale reliabil-ity in the social sciences.Indeed,the number of Google citations likely underestimates its use in practice.This is because coefficient alpha has become an icon,and,analogous to other statistical or psychometric icons,it is often no longer associated with its creator.Stated somewhat differently,its attribution or citation history in publications has progressed from “Cronbach’s coefficient alpha”to “coefficient alpha”to “alpha.”In addition to being widely applied,coefficient alpha has been widely investigated.Its measurement and statistical properties have extensively evaluated and discussions and arguments have both praised and criticized its use and interpretation.See,for example,Cortina (1993),Schmitt (1996),and Sijtsma (2009)for representative articles discussing the use and interpretation of coefficient alpha.Moreover,meta-analyses (e.g.,Peterson,1994;Rodriguez &Maeda,2006)have empirically explored factors that affect the values of coefficient alpha under a variety of conditions.The magnitude of coefficient alpha has often been the focus of investigation,with conclusions generally being that “alpha under-estimates the true reliability of a measure that is not tau equiva-lent”(Osburn,2000,p.344)or that “departure from classical tau-equivalence does lead to a small downward bias in alpha whenused as a composite reliability measure”(Bacon,Sauer,&Young,1995,p.394).Cronbach (2004,p.402)himself agreed with the assessment of a downward bias in coefficient alpha due to “a small mathematical detail that causes the alpha coefficient to run a trifle lower than the desired value.”More specifically,there is near universal agreement that unless there is (essential)tau equivalency,coefficient alpha is a lower bound on true reliability (e.g.,Novick &Lewis,1967;Sijtsma,2009).Because coefficient alpha tends to be viewed as a lower bound on true reliability,numerous alternative estimators of true reliability have been proffered.These alternative estima-tors include what are usually termed the stratified alpha coef-ficient (Cronbach,Schonemann,&McKie,1965);Raju coeffi-cient,Angoff-Feldt coefficient,Feldt coefficient,Feldt-Gimer coefficient,lambda2coefficient,mazimized lambda4coeffi-cient,standardized alpha coefficient,maximal reliability coef-ficient (summarized in Osburn,2000);and the beta coefficient (Revelle &Zinbarg,2009;Zinbarg,Revelle,Yovel,&Li,2005),among others.One category of proffered estimators consists of those that are based on structural equation modeling (e.g.,Bacon et al.,1995;Fornell &Larcker,1981;Graham,2006;Green &Yang,2009b;Heise &Bohrnstedt,1970;Jöreskog,1971;McDonald,1999;Raykov,1997;Yang &Green,2011).Given that the variance of observed scale scores in classical test theory can be decomposed asX 2ϭT 2ϩE 2,where X represents the observed test or scale score variance,T represents the true test or scale score variance,and E representserror variance,the true reliability of a test or scale ϭ⌻2/X 2.Thus,the reliability of a test or scale is represented by the ratio of its true score variance divided by its observed score variance.As such,the variance components can be estimated using structural equation modeling.When true reliability is estimated using struc-tural equation modeling,the resulting estimate is typically referred to as composite reliability (CR).The claimed benefits of a struc-tural equation modeling approach include “better”(e.g.,typicallyThis article was published Online First November 5,2012.Robert A.Peterson and Yeolib Kim,Office of the Vice President for Research,The University of Texas at Austin.Correspondence concerning this article should be addressed to Robert A.Peterson,Office of the Vice President for Research,The University of Texas at Austin,Austin,TX 78712.E-mail:rap@Journal of Applied Psychology ©2012American Psychological Association 2013,Vol.98,No.1,194–1980021-9010/13/$12.00DOI:10.1037/a0030767194larger)estimates of true reliability than possible through coeffi-cient alpha because construct loadings or weights are allowed to vary,whereas the loadings or weights for coefficient alpha are constrained to be equal.Consequently,structural equation model-ing has the ability to empirically assess and overcome some of the limiting assumptions of coefficient alpha(e.g.,Raykov,2001).PurposeDespite the claimed advantages of a composite reliability coef-ficient over coefficient alpha(e.g.,Green&Yang,2009b;Raykov, 2001),to date there have been no empirical comparisons of cor-responding coefficient alpha and composite reliability values.Al-though composite reliability coefficients have been shown to be larger than coefficient alpha analytically,and through simulations, there have been no comparisons of the two coefficients based on practical applications using nonartificial data.Therefore,the re-search question addressed here using a meta-analysis approach is twofold:Does composite reliability unequivocally produce a“bet-ter”(i.e.,larger)estimate of true reliability than coefficient alpha under identical research conditions?If so,then what methodolog-ical characteristics,if any,systematically relate to possible differ-ences in estimates of true reliability produced by composite reli-ability and coefficient alpha?If coefficient alpha systematically underestimates true reliability relative to composite reliability,then using coefficient alpha to correct validity correlation coefficients for attenuation would lead to overstating the magnitude of relationships.This could especially be a problem when conducting a meta-analysis in which concerted efforts are made to correct effect sizes(e.g.,correlations)for attenuation to make them amenable to meaningful aggregation.In brief,the purpose of the present study was to assess the compa-rability of coefficient alpha and composite reliability values de-rived under applied research conditions.MethodThe specific objective of the present study was to compare values of coefficient alpha and composite reliability derived from the same data analyses under applied conditions.Consequently, the methodology used followed that of Peterson and Brown(2005) in their comparison of corresponding beta coefficients and corre-lation coefficients drawn from regression analyses of various be-havioral data sets.As Peterson and Brown(2005)noted,“Al-though such a comparison could be made using synthetic data, synthetic data often do not capture the nuances and relationships that exist in actual data”(p.177).The same logic follows here. Following an initial search of journals in the EBSCO,JSTOR, and GALE databases using such terms as coefficient alpha and composite reliability,24journals were selected from which cor-responding pairs of coefficient alpha and composite reliability measure values were harvested.The journals encompassed psy-chology(e.g.,Journal of Applied Psychology),marketing(e.g., Journal of Marketing),management(e.g.,Journal of Operations Management),and education(e.g.,Computers and Education). Every article published in these journals over the period1996 through2011was personally examined to determine whether the results of an empirical study were reported and,if so,whether the article reported corresponding coefficient alpha and composite reliability values.Because the metrics sought(i.e.,coefficient alpha and composite reliability values)were not necessarily men-tioned in article titles,abstracts,or meta-tags,computer-based data collection approaches typically used in meta-analyses were not appropriate.Consequently,a very labor-intensive examination process was undertaken to harvest the metrics of interest.(To illustrate the effort required,Shook,Ketchen,Hult,&Kacmar, 2004,estimated that only3%of the articles in management re-porting reliability coefficients reported both coefficient alpha and composite reliability.)A total of2,524pairs of coefficient alpha values and composite reliability values were obtained from327articles containing381 separate studies in24journals(the median number of pairs per journal was67).These values were based on responses from 155,308individuals.Analogous to Peterson and Brown(2005),all identified pairs of coefficient alpha and composite reliability val-ues were harvested,even those that might be considered outliers or reflect reporting-or publication-related errors.In addition to these values,data were obtained regarding the sample sizes underlying the studies and various methodological characteristics of the un-derlying tests or scales.The characteristics included the number of items comprising the test or scale whose reliability was being evaluated,the number of categories in the items,the type of sample providing the data(college students,consumers,business-people),the item format(only endpoint anchors,numerical values on item categories,verbal values on item categories),and whether there was an odd or even number of item categories.Other data collected included the mode of scale administration(self-report vs. interviewer),test or scale orientation(i.e.,whether focused on a study participant[respondent-centered],or an independent stimu-lus[stimulus-centered]),and the nature of the construct being measured(whether it served as a dependent or an independent variable in the analysis).Because the data consisted of pairs of reported values,data issues common to meta-analyses including the file-drawer problem and reporting errors were not deemed problematic(see below).To facilitate comparisons,all coefficient alpha and composite reliability values were rounded to two deci-mal places prior to analysis.ResultsTable1contains characteristics of the respective distributions of the coefficient alpha and composite reliability values obtained from the meta-analysis.On average,based on a pairwise compar-ison of the two values,composite reliability values exceeded coefficient alpha values by about.018units,or2.1%,with a standard deviation of.047.Although this difference was statisti-Table1Characteristics of Coefficient Alpha and CompositeReliability(CR)Characteristic␣distribution CR distribution Range0.46–0.990.54–0.99 Mean.84.86 Median.86.88 Standard deviation.08.07 SkewnessϪ.80Ϫ1.04 Kurtosis.62 1.24195COEFFICIENT ALPHA AND COMPOSITE RELIABILITYcally significant (p Ͻ.001),significance was due more to the relatively large sample size and repeated measure analysis than a substantive difference.More specifically,there was no difference between coefficient alpha and composite reliability values in 27%of the observations;50%of the differences in values were within Ϯ.01units;and approximately 61%of the values within a pair were within Ϯ2%of each other.Simultaneously,though,in 59%of the observations,composite reliability values were larger than coefficient alpha values;coefficient alpha values were larger than composite reliability values in 15%of the observations.The correlation between coefficient alpha and composite reliability values was .80.Figure 1contains a scatter diagram of the pairs of coefficient alpha and composite reliability values.Table 2contains a summary of the results of one-way repeated measures analyses of variance,with coefficient alpha and compos-ite reliability being the dependent variables and research method-ology characteristics being the independent variables.(Subsample sizes do not always equal the total sample size for a particular characteristic due to missing data.)Although the vast majority of the differences between coefficient alpha and composite reliability values for particular methodological characteristics were statisti-cally significant,this was due more to the relatively large number of paired values analyzed than the magnitude of the differences.The important thing to note from Table 2is that the relationship between coefficient alpha and composite reliability mean values was relatively consistent across the methodological characteristics.An alternative way to examine the relationships between coef-ficient alpha and composite reliability is to explore a subset of the observations in which coefficient alpha values were larger than composite reliability values.This was done by creating a category of observations wherein coefficient alpha values were at least .05units larger than the corresponding composite reliability values (in other words,the differences in values were greater than 1SD ).There were 134such parison of the research methodology characteristics,respectively,associated with the 134observations,and the remaining 2,390observations revealed only one distinguishing difference.Coefficient alpha values were more likely to be larger than composite reliability values when stimulus-centered measurement was undertaken.To assess the possibility of publication or availability bias,we conducted two ancillary analyses.First,we obtained independent samples of coefficient alpha and composite reliability values from articles in the searched journals reporting one of the values,but not both.Specifically,153coefficient alpha values and 153composite reliability values were independently harvested from the 24jour-Figure 1.Scatterplot of coefficient alpha and composite reliabilityvalues.Table 2Coefficient Alpha and Composite Reliability Values for Research CharacteristicsCharacteristics N Mean ␣Mean CR Sample size Ͻ100ءء910.820.88100–199ءء6970.840.85200–299ءء6910.850.87300or more ءء1,0450.850.86Type of sampleCollege students ءء3500.860.89Consumers ءء6960.860.87Businesspersons ءء1,3600.830.85Mixed ءء1060.870.88Number of item categories Not given 1610.850.854120.770.785ءء8520.830.856ء230.850.857ءء1,4440.850.878or more ء310.870.89Number of items Not given 760.850.852ءء2470.800.833ءء9000.830.854ءء6390.850.875ءء3180.870.886ءء1800.880.897ءء750.870.908280.880.899190.890.9210100.920.9411or more 320.890.89Item formatOnly endpoints labeled ءء3900.850.87Numerical values on categories ءء5310.840.86Verbal values on categories ءء1,2390.840.86Cannot tell ءء3640.840.85Nature of scale Odd number ءء2,2930.840.86Even number 500.830.84Cannot tell1810.840.84Administrative mode Self ءء2,3270.840.86Interviewer ءء1790.860.88Not given ء180.900.86Scale orientationRespondent-centered ءء1,2950.850.87Stimulus-centered ءء1,0300.840.85Both ءء1990.850.87Nature of construct Dependent ءء5150.850.86Independent ءء1,2620.840.85Cannot tell/both ءء7470.860.87Note .CR ϭcomposite reliability.ءMean difference significant at p Ͻ.01.ءءMean difference significant atp Ͻ.001.196PETERSON AND KIMnals in the meta-analysis.The average coefficient alpha value was .83;the average composite reliability value was.85.Second,we harvested22pairs of coefficient alpha and composite reliability values from manuscripts submitted for publication consideration but not accepted(from the first author’s reviewer files).In this set, the mean coefficient alpha value was.87;the mean composite reliability value was.88.Thus,although the data were somewhat limited,the ancillary analyses suggest that the results of the meta-analysis are reasonably general.Discussion and ConclusionsThe answer to the first question underlying in this research,“... does composite reliability unequivocally produce a‘better’(i.e., larger)estimate of true reliability than coefficient alpha under identical research conditions?”is a qualified“yes,but.”The an-swer to the second question underlying this research,“...what methodological characteristics,if any,systematically relate to pos-sible differences in estimates of true reliability produced by com-posite reliability and coefficient alpha?”is a qualified“virtually none.”Estimates of true reliability produced by composite reliability were,on average,larger than those produced by coefficient alpha. This was expected given the formulaic differences in calculating the two coefficients(coefficient alpha is a constrained version of composite reliability).For the entire sample of observations,the mean composite reliability value was.86,whereas the mean co-efficient alpha value was.84;the mean difference calculated on individual pairs of the two values was.018.From a technical perspective,the.018difference could be interpreted as represent-ing the extent to which the assumptions underlying the calculation of coefficient alpha have not been met.The magnitude of the difference was relatively consistent across the various method-ological characteristics studied.The empirically derived means and mean difference observed in this study generally corroborate results reported in methodologi-cally oriented comparisons of coefficient alpha and composite reliability.For example,Green and Yang(2009b)calculated23 pairs of coefficient alpha and(linear)composite reliability values under a variety of artificial data conditions(see their Table1). Their mean coefficient alpha value was.598and their mean (linear)composite reliability value was.605;the mean difference was.007.They also reported a coefficient alpha value of.778and a(linear)composite reliability value of.819(difference of.041) for an eight-item scale administered to828individuals.Ferketich (1990)presented an example comparing coefficient alpha and composite reliability based on a10-item scale administered to590 individuals.The reported coefficient alpha value was.8446, whereas the reported composite reliability value was.8649;the difference was.0203.Raykov(1997)calculated a coefficient alpha value of.896and a composite reliability value of.912(difference of.016)for an eight-item scale administered to165individuals. Bacon et al.(1995)reported a coefficient alpha value of.596and a composite reliability value of.651(difference of.055)for a simulated data set.The overall similarity of the differences be-tween the two reliability coefficients across the various studies suggests that there is little practical difference between them. Even though composite reliability values observed in the present study were,on average,consistently“better,”that is,larger,than corresponding coefficient alpha values,in general the differences were not practically meaningful such that use of coefficient alpha to correct for attenuation would not be expected to systematically “inflate”the value of a validity coefficient(i.e.,mean␣1/2ϭ.917; mean CR1/2ϭ.927)relative to composite reliability when appliedin the standard attenuation correction formula.Moreover,the finding that about61%of the values in a pair were withinϮ2%of each other suggests that the differences in values were generally of little practical significance(e.g.,Lee&Frisbie,1999).Thus, although composite reliability values were typically larger than corresponding coefficient alpha values,claims that coefficient alpha grossly underestimates true reliability as compared with composite reliability need to be tempered.Accordingly,it is instructive to note that although composite reliability values were typically larger than coefficient alpha val-ues,in15%of the pairs of values coefficient alpha was larger than composite reliability;in about6%of the pairs,coefficient alpha values were significantly larger than corresponding composite reliability values.These percentages support the conclusions of Bentler(2009),Green and Yang(2009a),and Raykov(2001)that, under certain conditions,such as when there are correlated errors among the items in a test or scale,values of coefficient alpha may exceed values of composite reliability.Unfortunately,it was not possible to determine the specific reasons for these patterns of values from the present data.Possible reasons include rounding differences,inappropriate structural models used when estimating composite reliability,reporting errors in the values harvested, computational differences,or certain types of correlated data er-rors.Likewise,it was not possible to discern the reasons for those instances in which composite reliability values significantly ex-ceeded coefficient alpha values.Such differences may also be due to rounding or reporting errors,certain data configurations,or even misapplication of the two reliability coefficients(e.g.,when rela-tionships are formative rather than reflective,reliability coeffi-cients are not meaningful).More research is required to identify conditions leading to values of coefficient alpha being larger than values of composite reliability.The results of the present study suggest that,at a minimum, coefficient alpha and composite reliability values might be used interchangeably when correcting validity coefficients or effect sizes in meta-analyses with few practical consequences.Although coefficient alpha values may generally be lower bounds on true reliability,their use in practice should not be deleterious to knowl-edge development.ReferencesBacon,D.R.,Sauer,P.L.,&Young,M.(1995).Composite reliability in structural equations cational and Psychological Measure-ment,55,394–406.doi:10.1177/0013164495055003003Bentler,P.M.(2009).Alpha,dimension-free,and model-based internal consistency reliability.Psychometrika,74,137–143.doi:10.1007/ s11336-008-9100-1Cortina,J.M.(1993).What is coefficient alpha?An examination of theory and applications.Journal of Applied Psychology,78,98–104.doi: 10.1037/0021-9010.78.1.98Cronbach,L.J.(1951).Coefficient alpha and the internal structure of tests. Psychometrika,16,297–334.doi:10.1007/BF02310555197COEFFICIENT ALPHA AND COMPOSITE RELIABILITYCronbach,L.J.(2004).My current thoughts on coefficient alpha and successor cational and Psychological Measurement,64, 391–418.doi:10.1177/0013164404266386Cronbach,L.J.,Schonemann,P.,&McKie,D.(1965).Alpha coefficients for stratified-parallel cational and Psychological Measure-ment,25,291–312.doi:10.1177/001316446502500201Ferketich,S.(1990).Internal consistency estimates of reliability.Research in Nursing&Health,13,437–440.doi:10.1002/nur.4770130612 Fornell,C.,&Larcker,D.F.(1981).Evaluating structural equation models with unobservable variables and measurement error.Journal of Market-ing Research,18,39–50.doi:10.2307/3151312Graham,J.M.(2006).Congeneric and(essentially)tau-equivalent esti-mates of score cational and Psychological Measurement, 66,930–944.doi:10.1177/0013164406288165Green,S.B.,&Yang,Y.(2009a).Commentary on coefficient alpha:A cautionary tale.Psychometrika,74,121–135.doi:10.1007/s11336-008-9098-4Green,S.B.,&Yang,Y.(2009b).Reliability of summed item scores using structural equation modeling:An alternative to coefficient alpha.Psy-chometrika,74,155–167.doi:10.1007/s11336-008-9099-3Heise,D.R.,&Bohrnstedt,G.W.(1970).Validity,invalidity,and reli-ability.In E.F.Borgatta&G.W.Bohrnstedt(Eds.),Sociological methodology(pp.104–129).San Francisco,CA:Jossey-Bass.Jöreskog,K.G.(1971).Statistical analysis of sets of congeric tests. Psychometrika,36,109–133.doi:10.1007/BF02291393Lee,G.,&Frisbie,D.A.(1999).Estimating reliability under a generaliz-ability theory model for test scores composed of testlets.Applied Mea-surement in Education,12,237–255.doi:10.1207/S1*******AME1203_2 McDonald,R.P.(1999).Test theory:A unified treatment.Mahwah,NJ: Erlbaum.Novick,M.R.,&Lewis,C.(1967).Coefficient alpha and the reliability of composite measurements.Psychometrika,32,1–13.doi:10.1007/ BF02289400Osburn,H.G.(2000).Coefficient alpha and related internal consistency reliability coefficients.Psychological Methods,5,343–355.doi: 10.1037/1082-989X.5.3.343Peterson,R.A.(1994).A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research,21,381–391.doi:10.1086/209405 Peterson,R.A.,&Brown,S.P.(2005).On the use of beta coefficients in meta-analysis.Journal of Applied Psychology,90,175–181.doi: 10.1037/0021-9010.90.1.175Raykov,T.(1997).Estimation of composite reliability for congeneric measures.Applied Psychological Measurement,21,173–184.doi: 10.1177/01466216970212006Raykov,T.(2001).Bias of coefficient␣for fixed congeneric measures with correlated errors.Applied Psychological Measurement,25,69–76. doi:10.1177/01466216010251005Revelle,W.,&Zinbarg,R.E.(2009).Coefficients alpha,beta,omega,and the glb:Comments on Sijtsma.Psychometrika,74,145–154.doi: 10.1007/s11336-008-9102-zRodriguez,M.C.,&Maeda,Y.(2006).Meta-analysis of coefficient alpha. Psychological Methods,11,306–322.doi:10.1037/1082-989X.11.3.306 Schmitt,N.(1996).Uses and abuses of coefficient alpha.Psychological Assessment,8,350–353.doi:10.1037/1040-3590.8.4.350Shook,C.L.,Ketchen,D.J.,Jr.,Hult,G.T.M.,&Kacmar,K.M.(2004). An assessment of the use of structural equation modeling in strategic management research.Strategic Management Journal,25,397–404. doi:10.1002/smj.385Sijtsma,K.(2009).On the use,the misuse,and the very limited usefulness of Cronbach’s alpha.Psychometrika,74,107–120.doi:10.1007/s11336-008-9101-0Yang,Y.,&Green,S.B.(2011).Coefficient alpha:A reliability coefficient for the21st century?Journal of Psychoeducational Assessment,29, 377–392.doi:10.1177/0734282911406668Zinbarg,R.E.,Revelle,W.,Yovel,I.,&Li,W.(2005).Cronbach’s␣, Revelle’s,and McDonald’sH:Their relations with each other and two alternative conceptualizations of reliability.Psychometrika,70, 123–133.doi:10.1007/s11336-003-0974-7Received January3,2012Revision received October1,2012Accepted October2,2012Ⅲ198PETERSON AND KIM。
Zhang-Yang波动率计算

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The price range, defined as the difference between the highest and lowest market prices over a fixed sampling interval, has been known for a long time and recently experienced renewed interest as an estimator of the latent volatility. The information contained in the opening, highest, lowest, and closing prices of an asset is widely used in Japanese candlestick charting techniques and other technical indicators (Nisson, 1991). Early application of range in the field of finance can be traced to Mandelbrot (1971), and the academic work on the range-based volatility estimator started from the early 1980s. Several authors, back to Parkinson (1980), developed from it several volatility measures far more efficient than the classical return-based volatility estimators.
空间相关性与分类比例在不同抽样设计中对准确性测量的影响——翻译

空间相关性与分类比例在不同抽样设计中对准确性测量的影响DongMei Chen_, Hui Wei加拿大,金士顿ON K7L 3N6,皇后大学,地理系摘要:本文仿真了四幅结合两种空间相关性标准、两种不同的分类比例的二进制的专题图以研究借助不同抽样设计的分类准确性效果。
11种抽样类型(从25到1296)按3种常用的抽样设计,包括简单随机抽样(SRS),系统抽样(SYS),分层随机抽样(StrRS)在4幅仿真地图上被仿真。
常见的误差矩阵和相关的准确性度量也被考虑进每一个仿真中。
同时,3种抽样设计的不同精度估计也相互比较。
某一特别的抽样方法和抽样类型的选择依赖于空间相关性的程度、分类比例的差异以及应用中所需的精度要求。
通常,一幅地图分类比例的差异比空间相关性对抽样方法的效果影响更大。
对于估计个体类的精度,特别在小型类中StrRS的效果比SRS和SYS的效果更好。
对于估计总精确度,不同的抽样设计的效果相似。
为了得到更好的KAPPA系数,推荐StrRS用于高类别比例差异的地图,推荐SRS用于低空间相关性和低类别比例差异的地图。
关键词:精度评估;分类误差;抽样;分类比例1.引言随着遥感技术的发展,从遥感数据得到的专题地图被广泛应用于不同的环境建模,监控,以及计划中。
但这些专题地图通常不能完美的代表现实,还常常包含误差和不确定性(Foody,2002)。
在设计中使用这样的地图却不知道其中的误差和不确定性会导致严重的风险。
因此,在最近10年,对专题地图的准确性评估的需要不断增长(Congalton and Green,1999;Congalton and Plourde,2002;Congalton,2002; Foody,2002;Lunetta and Lyon,2004;Stehman and Czaplewski,1998)。
基于遥感的专题图的精确度涉及到一幅地图或分类的正确性(Foody,2002),或者涉及导出信息与参照数据(或地面实况)相吻合的程度(Campbell 1996)。
超声测距外文-超声波距离和速度利用互相关方法对LPM信号测量

Ultrasonic distance and velocity measurement using a pair of LPM signals for cross-correlation method:Improvement of Doppler-shift compensation and examination of Doppler velocity estimation超声波距离和速度利用互相关方法对LPM信号测量:多普勒频移补偿和多普勒速度估计检测的改进数据来源Elsevier Journal Elsevier期刊刊物名Ultrasonics, 2012, Vol.52 (7), pp.873-879 超声波,2012,卷(7),pp.873-879 作者Shinnosuke Hirata, Minoru Kuribayashi Kurosawashinnosuke平田,稔栗林黑泽明单位机械工程与智能系统1,信息工程学院,电子通信,1-5-1 chofugaoka e4-329,,,布,东京182-8585大学,日本信息处理系,跨学科研究生科学与工程学院,东京工业大学,4259首席人事官g2-32,长津田,绿区,横滨,神奈川226-8502,日本AbstractReal-time distance measurement of a moving object with high accuracy and high resolution using an ultrasonic wave is difficult due to the influence of the Doppler effect or the limit of the calculation cost of signal processing. An over-sampling signal processing method using a pair of LPM signals has been proposed for ultrasonic distance and velocity measurement of moving objects with high accuracy and high resolution. The proposed method consists of cross correlation by single-bit signal processing, high-resolution Doppler velocity estimation with wide measurement range and low-calculation-cost Doppler-shift compensation. The over-sampling cross-correlation function is obtained from cross correlation by single-bit signal processing with low calculation cost. The Doppler velocity and distance of the object are determined from the peak interval and peak form in the cross-correlation function by the proposed method of Doppler velocity estimation and Doppler-shift compensation. In this paper, the proposed method of Doppler-shift compensation is improved. Accuracy of the determined distance was improved from approximately within ±140 μm in the previous method to approximately within ±10μm in computer simulations. Then, the proposed method of Doppler velocity estimation is evaluated. In computer simulations, accuracy of the determined Doppler velocity and摘要实时测量移动物体的高精度和高分辨率超声波存在的多普勒效应或信号处理的计算成本的限制的影响。
独树一帜还是随波逐流

费者会将自身的需求放在首位ꎬ 更在意产品的独特性和新颖性ꎬ 产品效用会随购买人数增加而降
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珞珈管理评论
2024 年卷第 2 辑 ( 总第 53 辑)
低ꎻ 跟随型消费者则把其他消费者的期望或行为作为自己行为参照的准则ꎬ 进而在自己的产品评
管理评论
Luojia Management Review
2024 年卷第 2 辑 ( 总第 53 辑)
No 2ꎬ 2024 ( Sum 53)
独树一帜还是随波逐流?
消费者类型和奢侈品环保信息
交互效应对购买意愿的影响
∗
•冯文婷1 李 洁2 沈先运2 刘陈陵2
(1 中国地质大学 ( 武汉) 珠宝学院 武汉 430074ꎻ
侈品品牌热衷于投身可持续性实践活动ꎬ 开发可持续奢侈品的生产线ꎬ 可持续奢侈品日益成为一种
新的时尚潮流 ( Li & Leonasꎬ 2019) ꎮ
可持续奢侈品是指符合环保诉求ꎬ 具有节能、 无害等可持续属性或相关联的奢侈品ꎮ 现有研究
表明ꎬ 消费者对于可持续奢侈品存在两种矛盾态度: 一方面ꎬ “ 可持续” 与自我超越、 道德和利他主
目ꎻ 阿玛尼则承诺 2020 年起避免在生产过程中使用危险化学品ꎻ 古驰也在 2017 年宣布不再使用一切
动物皮草ꎮ 此外ꎬ 在全球可持续时尚峰会上ꎬ 宝格丽首席执行官提出: “ 作为一个奢侈品品牌ꎬ 宝格
丽以其可持续性发展的品牌理念为傲ꎬ 在每一个经营环节中坚持合乎道德的生产准则” ꎮ 这表明ꎬ 奢
实验二验证消费者心理需求 ( 分化、 同化) 和奢侈品环保信息 ( 可持续、 非可持续) 的匹配一致性
海外文献原文-推荐参考文献列表

海外文献推荐-第一期参考文献:[1] I-Cheng Yeh, Che-Hui Lien, Tao-Ming Ting, 2015, Building multi-factor stock selection models using balanced split regression trees with sorting normalisation and hybrid variables, Foresight and Innovation Policy, V ol. 10, No. 1, 48-74[2] Eugene F.Fama, KennethR.French, 2015, A Five-factor Asset Pricing Model, Journal of Financial Economics 116, 1-22[3] Achim BACKHAUS, Aliya ZHAKANOV A ISIKSAL, 2016, The Impact of Momentum Factors on Multi Asset Portfolio, Romanian Journal of Economic Forecasting XIX (4), 146-169[4] Francisco Barillas, Jay Shanken, 2016, Which Alpha? Review of Financial Studies海外文献推荐-第二期参考文献:[1] PRA VEEN KUMAR, DONGMEI LI, 2016, Capital Investment, Innovative Capacity, and Stock Returns, The Journal of Finance, VOL. LXXI, NO. 5, 2059-2094[2] Houda Ben Mabrouk, Abdelfettah Bouri, 2013, New insight on the CAPM: a copula-based approach Tunisian and international evidence, Accounting and Finance, Vol. 4, No. 1, 35-62 [3] FERHAT AKBAS, 2016, The Calm before the Storm, The Journal of Finance, VOL. LXXI, NO. 1,225-266海外文献推荐-第三期参考文献:[1] Yufeng Han, Guofu Zhou, Yingzi Zhu, 2016, A trend factor: Any economic gains from using information over investment horizons? Journal of Financial Economics 122, 352-375[2] Andrea Frazzini, LasseHeje Pedersen, 2014, Betting against beta, Journal of Financial Economics 111, 1-25[3] Doron Avramov, Si Cheng, and Allaudeen Hameed, 2016, Time-Varying Liquidity and Momentum Profits, JOURNAL OF FINANCIAL AND QUANTITATIVE ANAL YSI, Vol. 51, No. 6, 1897-1923[4] Nicholas Barberis, Abhiroop Mukherjee, Baolian Wang, 2014, Prospect Theory and Stock Returns: An Empirical Test, Review of Financial Studies海外文献推荐-第四期参考文献:[1] Brad M. Barber, Xing Huang, Terrance Odean, 2014, Which risk factors matter to investors? Evidence from mutual fund flows, Review of Financial Studies[2] MICHAEL J. COOPER, HUSEYIN GULEN, & MICHAEL J. SCHILL. (2008). Asset growth and the cross‐section of stock returns. Social Science Electronic Publishing, 63(4), 1609–1651.[3]Bollerslev, T., Li, S. Z., & Todorov, V. (2016). Roughing up beta: continuous versus discontinuous betas and the cross section of expected stock returns. Journal of Financial Economics, 120(3), 464-490.[4]Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment ⋆. Journal of Financial Economics, 104(2), 272-287.海外文献推荐-第五期参考文献:[1] Nicole Choi, Mark Fedenia, Tatyana Sokolyk, 2017, Portfolio Concentration and Performance of Institutional Investors Worldwide, Journal of Financial Economics[2]Cronqvist, H., Siegel, S., & Yu, F. (2015). Value versus growth investing: why do differentinvestors have different styles? ☆. Journal of Financial Economics, 117(2), 333-349.[3]Rapach, D. E., Ringgenberg, M. C., & Zhou, G. (2016). Short interest and aggregate stock returns . Journal of Financial Economics, 121(1), 46-65.[4]Novy-Marx, R. (2013). The other side of value: the gross profitability premium ☆. Journal of Financial Economics, 108(1), 1-28.海外文献推荐-第六期参考文献:[1] Suk Joon Byun, Sonya S. Limy, and Sang Hyun Yun, 2012, Continuing Overreaction and Stock Return Predictability, Journal of Financial and Quantitative Analysis[2]Eugene F. Fama, & Kenneth R. French. (2016). International tests of a five-factor asset pricing model. Journal of Financial Economics, 123.[3]Keloharju, M., Linnainmaa, J. T., & Nyberg, P. (2016). Return seasonalities. Journal of Finance, 71(4), n/a-n/a.[4]Seasholes, M. S., & Wu, G. (2007). Predictable behavior, profits, and attention. Journal of Empirical Finance, 14(5), 590-610.[5]PA VEL SAVOR, & MUNGO WILSON. (2016). Earnings announcements and systematic risk. The Journal of Finance, 71(1).海外文献推荐-第七期参考文献:[1] Cary Frydman and Colin Camerer, 2016, Neural Evidence of Regret and its Implications for Investor Behavior, Review of Financial Studies 29, 3108-3139[2] Haghani, V., & Dewey, R. (2016). A case study for using value and momentum at the asset class level. Journal of Portfolio Management, 42(3), 101-113.[3] Tarun, C., Amit, G., & Narasimhan, J. (2011). Buyers versus sellers: who initiates trades, and when?. Journal of Financial & Quantitative Analysis, 51(5), 1467-1490.[4] Hartzmark, M. S. (2015). The worst, the best, ignoring all the rest: the rank effect and trading behavior. Review of Financial Studies, 28(4), 1024.[5] Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial Economics, 122(2), 221-247.海外文献推荐-第八期参考文献:[1]Hua, R., Kantsyrev, D., & Qian, E. (2012). Factor-timing model.Journal of Portfolio Management,39(1), 75-87.[2]Leshem, R., Goldberg, L. R., & Cummings, A. (2015). Optimizing value.Journal of Portfolio Management,42(2).[3]Chemmanur, Thomas J., Gang Hu and Jiekun Huang, 2015, Institutional Investors and the Information Production Theory of Stock Splits,Journal of Financial and Quantitative Analysis50(3), 413–445.海外文献推荐-第九期参考文献:[1]Penaranda, F. (2016). Understanding portfolio efficiency with conditioning information. Economics Working Papers, 51(3), 985-1011.[2]Cederburg, S., & O'Doherty, M. S. (2016). Does it pay to bet against beta? on the conditional performance of the beta anomaly. Journal of Finance, 71(2), 737-774.[3]Lindsey, R. R., & Weisman, A. B. (2016). Forced liquidations, fire sales, and the cost of illiquidity. Journal of Portfolio Management, 20(1), 45-57.海外文献推荐-第十期参考文献:[1] Easley, D., Hvidkjaer, S., & O'Hara, M. (2010). Factoring information into returns. Journal of Financial & Quantitative Analysis, 45(2), 293-309.[2]Babenko, I., Boguth, O., & Tserlukevich, Y. (2016). Idiosyncratic cash flows and systematic risk. Journal of Finance, 71(1).[3]Chow, V., & Lai, C. W. (2015). Conditional sharpe ratios. Finance Research Letters, 12, 117-133.海外文献推荐-第十一期参考文献:[1] Mladina, P. (2017). Illuminating hedge fund returns to improve portfolio construction. Social Science Electronic Publishing, 41(3), 127-139.[2] Choi, N., Fedenia, M., Skiba, H., & Sokolyk, T. (2016). Portfolio concentration and performance of institutional investors worldwide. Journal of Financial Economics.[3] Martijn Boons, 2016, State variables, macroeconomic activity, and the cross section of individual stocks, Journal of Financial Economics 119, 489-511海外文献推荐-第十二期参考文献:[1] Blanchett, D., & Ratner, H. (2015). Building efficient income portfolios. Journal of Portfolio Management, 41(3), 117-125.[2] Özde Öztekin. (2015). Capital structure decisions around the world: which factors are reliably important?. Journal of Financial & Quantitative Analysis, 50(3).[3] 2015, Does the number of stocks in a portfolio influence performance? Investment Sights海外文献推荐-第十三期参考文献:[1]Glushkov, D., & Statman, M. (2016). Classifying and measuring the performance of socially responsible mutual funds.Social Science Electronic Publishing,42(2), 140-151.[2]KLAUS ADAM, ALBERT MARCET, & JUAN PABLO NICOLINI. (2016). Stock market volatility and learning.The Journal of Finance,71(1), 419–438.[3]Miller, K. L., Li, H., Zhou, T. G., & Giamouridis, D. (2012). A risk-oriented model for factor timing decisions.Journal of Portfolio Management,41(3), 46-58.海外文献推荐-第十四期参考文献:[1]Feldman, T., Jung, A., & Klein, J. (2015). Buy and hold versus timing strategies: the winner is ….Journal of Portfolio Management,42(1), 110-118.[2]Eric H Sorensen, Nicholas F Alonso. The Resale Value of Risk-Parity Equity Portfolios[J]. Journal of Portfolio Management, 2015, 41(2):23-32.海外文献推荐-第十五期参考文献:[1]Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments ☆.Journal of Financial Economics,116(1), 111-120.[2]Bender, J., & Nielsen, F. (2015). Earnings quality revisited.Social Science Electronic Publishing,39(4), 69-79.海外文献推荐-第十六期参考文献:[1]Greenberg, D., Abhilash, B., & Ang, A. (2016). Factors to assets: mapping factor exposures to asset allocations. Journal of Portfolio Management, 42(5), 18-27.[2]Goyal, A., Ilmanen, A., & Kabiller, D. (2015). Bad habits and good practices. Journal of Portfolio Management, 41(4), 97-107.海外文献推荐-第十七期参考文献:[1]Vermorken, M. A., Medda, F. R., & Schröder, T. (2012). The diversification delta: a higher-moment measure for portfolio diversification. Journal of Portfolio Management, 39(1), 67-74.[2]Asl, F. M., & Etula, E. (2012). Advancing strategic asset allocation in a multi-factor world.Journal of Portfolio Management,39(1), 59-66.海外文献推荐-第十八期参考文献:[1]Chakrabarty, B., Moulton, P. C., & Trzcinka, C. (2016). The performance of short-term institutional trades. Social Science Electronic Publishing, 1-26.[2]Stubbs, R. A., & Jeet, V. (2015). Adjusted Factor-Based Performance Attribution. USXX.海外文献推荐-第十九期参考文献:[1]Copeland, M., & Copeland, T. (2016). Vix versus size. Journal of Portfolio Management, 42(3), 76-83.[2]Kritzman, M., & Turkington, D. (2016). Stability-adjusted portfolios. Journal of Portfolio Management, 42(5), 113-122.海外文献推荐-第二十期参考文献:[1]Benos, E., Brugler, J., Hjalmarsson, E., & Zikes, F. (2016). Interactions among high-frequency traders. Journal of Financial & Quantitative Analysis, 52, 1-28.[2]Richardson, S., Sloan, R., & You, H. (2011). What makes stock prices move? fundamentals vs. investor recognition. Financial Analysts Journal, 68(2), 30-50.海外文献推荐-第二十一期参考文献:[1]Bogousslavsky, V. (2016). Infrequent rebalancing, return autocorrelation, and seasonality. Journal of Finance, 71(6), 2967-3006.[2]Marcos, L. D. P. (2015). The future of empirical finance. Journal of Portfolio Management, 41(4), 140-144.海外文献推荐-第二十二期参考文献:[1] Fabian, H., & Marcel, P. (2016). Estimating beta. Journal of Financial & Quantitative Analysis, 51(4), 1437-1466.[2] Christopher Cheung, George Hoguet, & Sunny Ng. (2014). Value, size, momentum, dividend yield, and volatility in china’s a-share market. Journal of Portfolio Management, 41(5), 57-70.海外文献推荐-第二十三期参考文献:[1]Mclean, R. D., & Zhao, M. (2014). The business cycle, investor sentiment, and costly external finance.Journal of Finance, 69(3), 1377–1409.[2]Kaniel, R., & Parham, R. (2017). The impact of media attention on consumer and mutual fund investment decisions. Journal of Financial Economics, 123, págs. 337-356海外文献推荐-第二十四期参考文献:[1]Chang, X., Chen, Y., & Zolotoy, L. (2017). Stock liquidity and stock price crash risk. Journal of Financial & Quantitative Analysis.[2]Bisetti, E., Favero, C. A., Nocera, G., & Tebaldi, C. (2013). A multivariate model of strategic asset allocation with longevity risk. Ssrn Electronic Journal.海外文献推荐-第二十五期参考文献:[1] Lou, X., & Shu, T. (2013). Price impact or trading volume: why is the amihud (2002) measure priced?. Social Science Electronic Publishing.[2]Lins, K. V., Servaes, H., & Tamayo, A. (2017). Social capital, trust, and firm performance: the value of corporate social responsibility during the financial crisis. Journal of Finance, 72.海外文献推荐-第二十六期参考文献:[1] Golez, B., & Koudijs, P. (2014). Four centuries of return predictability. Social Science Electronic Publishing.[2]Ledoit, O., and Wolf, M. (2017). Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks. The Review of Financial Studies, 30(12), 4349-4388.海外文献推荐-第二十七期参考文献:[1]Ray Dalio, Bob Prince, Greg Jensen (2015), our thoughts about risk parity and all weather, Bridgewater Associates, LP[2]Thierry, R. and Guillaume, W. (2013). Risk Parity Portfolios with Risk Factors. MPRA Paper No. 44017.海外文献推荐-第二十八期参考文献:[1] Golubov, A., & Konstantinidi, T. (2015). Where is the risk in value? evidence from a market-to-book decomposition. Social Science Electronic Publishing.[2] Moreira, A., and Muir, T. (2017). Volatility‐Managed Portfolios. Journal of Finance, 72(4).海外文献推荐-第二十九期参考文献:[1]Wahalab S. Style investing, comovement and return predictability ☆[J]. Journal of Financial Economics, 2013, 107(1).[2]Pástor Ľ, Stambaugh R F, Taylor L A. Do funds make more when they trade more?[J]. The Journal of Finance, 2017, 72(4): 1483-1528.海外文献推荐-第三十期参考文献:[1] K Hou, C Xue, L Zhang, Digesting Anomalies: An Investment Approach, NBER Working Papers, 2015, 28(3)[2]Berk, J. B., & Binsbergen, J. H. V. (2013). Measuring skill in the mutual fund industry. Journal of Financial Economics, 118(1), 1-20.海外文献推荐-第三十一期参考文献:[1]Klein, Rudolf F. and V. K. Chow. "Orthogonalized factors and systematic risk decomposition." Quarterly Review of Economics & Finance 53.2(2013):175-187.[2]Sorensen E H, Hua R, Qian E E. Contextual Fundamentals, Models, and Active Management[J]. Journal of Portfolio Management 32.1(2005):23-36.海外文献推荐-第三十二期参考文献:[1] Hong, H. Torous, W. & Valkanov, R. (2007). Do industries lead stock markets? Journal of Financial Economics,83 (2), 367-396.[2]Dhillon, J. Ilmanen, A. & Liew, J. (2016). Balancing on the life cycle: target-date funds need better diversification. Journal of Portfolio Management, 42(4), 12-27.海外文献推荐-第三十三期参考文献:[1]Kenneth Froot and Melvyn Teo, Style Investing and Institutional Investors, JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS V ol. 43, No. 4, Dec. 2008, pp. 883–906.[2]Israel R, Palhares D, Richardson S A. Common factors in corporate bond returns[J]. Social Science Electronic Publishing, 2015.海外文献推荐-第三十四期参考文献:[1] DM Smith, N Wang, Y Wang, EJ Zychowicz, Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry,Journal of Financial & Quantitative Analysis, 2016 , 51 (6) :1991-2013[2]Ronen Israel, Sarah Jiang, and Adrienne Ross (2018). 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Overnight Returns and Firm-Specific Investor Sentiment. Journal of Financial and Quantitative Analysis.[2] Arnott R, Beck N, Kalesnik V, et al. How Can 'Smart Beta' Go Horribly Wrong?[J]. Social Science Electronic Publishing, 2017.海外文献推荐-第三十九期参考文献:[1] CS Asness, A Frazzini, LH PedersenDM, 2013,Quality Minus Junk,Social Science Electronic Publishing[2] Stein, M, & Rachev, S. T. (2011). Style-neutral funds of funds: diversification or deadweight? Journal of Asset Management, 11(6), 417-434.海外文献推荐-第四十期参考文献:[1] Li Y, Sun Q, Tian S. The impact of IPO approval on the price of existing stocks: Evidence from China[J]. Journal of Corporate Finance, 2018.[2] Jennifer Bender,Xiaole Sun,Ric Thomas,V olodymyr Zdorovtsov, The Journal of Portfolio Management , 2018 , 44 (4) :79-92海外文献推荐-第四十一期参考文献:[1] Yi Fang & Haiping Wang (2015) Fund manager characteristics and performance, Investment Analysts Journal, 44:1, 102-116.[2] Roni Israelov, Harsha Tummala. An Alternative Option to Portfolio Rebalancing. The Journal of Derivatives Spring 2018, 25 (3) 7-32海外文献推荐-第四十二期参考文献:[1] Robert Capone, Adam Akant, (2016), Trend Following Strategies in Target-Date Funds, AQR Capital Management.[2] Loh, R. K., & Stulz, R. M. (2018). Is sell‐side research more valuable in bad times?. Journal of Finance, 73(3): 959-1013.海外文献推荐-第四十三期参考文献:[1] Asness, C. S., Frazzini, A., Israel, R., & Moskowitz, T. J. (2015). Fact, fiction, and value investing. Final version published in Journal of Portfolio Management, V ol. 42, No.1[2] Gu, S., Kelly, B. T., & Xiu, D. (2018). Empirical asset pricing via machine learning. Social Science Electronic Publishing.海外文献推荐-第四十四期参考文献:[1] David P. Morton, Elmira Popova, Ivilina Popova, Journal of Banking & Finance 30 (2006) 503–518海外文献推荐-第四十五期参考文献:[1] Lleo, S., & Ziemba, W. T. (2017). A tale of two indexes: predicting equity market downturns in china. Social Science Electronic Publishing海外文献推荐-第四十六期参考文献:[1] Alquist, R., Israel, R., & Moskowitz, T. J. (2018). Fact, fiction, and the size effect. Social Science Electronic Publishing.[2] Kacperczyk M, NIEUWERBURGH S V A N, Veldkamp L. Time-varying fund manager skill[J]. The Journal of Finance, 2014, 69(4): 1455-1484.海外文献推荐-第四十七期参考文献:[1] Tom Idzorek, 2008, Lifetime Asset Allocations: Methodologies for Target Maturity Funds, Ibbotson Associates Research Paper,29-47[2] Da, Z., Huang, D., & Yun, H. (2017). Industrial electricity usage and stock returns. Journal of Financial & Quantitative Analysis, 52(1), 37-69.海外文献推荐-第四十八期参考文献:[1] Clifford Asness and Andrea Frazzini, 2013, The Devil in HML’s Details, The Journal of Portfolio Management, volume 39 number 4.[2] Carvalho, R. L. D., Xiao, L., & Moulin, P. (2011). 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The Journal of Investing. 2010 December海外文献推荐-第五十四期参考文献:[1]Cliff's Perspective, Our Model Goes to Six and Saves Value From Redundancy Along the Way,AQR Capital Management, December 17, 2014[2]D Avramov,S Cheng,A Schreiber,K Shemer,2017,Scaling up Market Anomalies,Social Science Electronic Publishing,26 (3) :89-105海外文献推荐-第五十五期参考文献:[1]Aurélien Philippot,Analysts’ reinitiations of coverage and market underreaction,Journal of Banking and Finance , 94 (2018) 208–220海外文献推荐-第五十六期参考文献:[1]Michael W. Brandt, Earnings Announcements are Full of Surprises,Social Science Electronic Publishing, January 22, 2008[2]Sujin Pyo, Jaewook Lee,Exploiting the low-risk anomaly using machine learning to enhance the Black–Litterman framework: Evidence from South Korea,Pacific-Basin Finance Journal,51 (2018) 1–12[3]Robert F Engle and Andrew J Patton,What good is a volatility model?,Robert F Engle and Andrew J Patton海外文献推荐-第五十七期参考文献:[1]Nic Schaub, The Role of Data Providers as Information Intermediaries,Social Science Electronic Publishing, 2015 :1-34海外文献推荐-第五十八期参考文献:[1]Binu George and Hardik Shah, ESG: Improving Your Risk-Adjusted Returns in Emerging Markets,GMO White Paper, Mar 2018海外文献推荐-第五十九期参考文献:[1]Campbell R. Harvey and Yan Liu. Backtesting. Journal of portfolio management, 2015海外文献推荐-第六十期参考文献:[1]Mclean R D, Pontiff J. Does Academic Research Destroy Stock Return Predictability?[J]. Journal of Finance, 2016, 71(1)海外文献推荐-第六十一期参考文献:[1]Israelov R, Tummala H. Which Index Options Should You Sell?[J]. Social Science Electronic Publishing, 2017海外文献推荐-第六十二期参考文献:[1]Eric H. Sorensen, Keith L. Miller, and Chee K. Ooi,2000,The Decision Tree Approach to Stock Selection,The Journal of Portfolio Management,42-52海外文献推荐-第六十三期参考文献:[1]Donangelo A, Gourio F, Kehrig M, et al. The cross-section of labor leverage and equity returns[J]. Journal of Financial Economics, 2018海外文献推荐-第六十四期参考文献:[1]Qang Bu. Do Persistent Fund Alphas Indicate Manager Skill? [J]. Journal of Wealth Management,2017,20(2)82-93海外文献推荐-第六十五期参考文献:[1]Miguel A. Lejeune A VaR Black–Litterman model for the construction of absolute return fund-offunds [J] Quantitative Finance · January 2009海外文献推荐-第六十六期参考文献:[1]Fan J H, Zhang T. Demystifying Commodity Futures in China [J]. Social Science Electronic Publishing, 2018海外文献推荐-第六十七期参考文献:[1]Jon Hale, Sustainable Funds U.S. Landscape Report. Morningstar Research, 2018.海外文献推荐-第六十八期参考文献:[1]Sun Z, Wang A, Zheng L. Only Winners in Tough Times Repeat: Hedge Fund Performance Persistence over Different Market Conditions[J]. Journal of Financial and Quantitative Analysis, 2018.海外文献推荐-第六十九期参考文献:[1] A´LVARO CARTEA,SEBASTIAN JAIMUNGAL. RISK METRICS AND FINE TUNING OF HIGH-FREQUENCY TRADING STRATEGIES [J]. Mathematical Finance, V ol. 00, No. 0 (xxx 2013), 1-36.海外文献推荐-第七十期参考文献:[1] Dopfel, Frederick E. , and L. Ashley . "Optimal Blending of Smart Beta and Multifactor Portfolios." The Journal of Portfolio Management 44.4(2018):93-105.海外文献推荐-第七十一期参考文献:[1] Avraham Kamara, Robert Korajczyk, Xiaoxia Lou and Ronnie Sadka,2018,Short-Horizon Beta or Long-Horizon Alpha?, The Journal of Portfolio Management,45(1),96-105海外文献推荐-第七十二期参考文献:[1] Masulis, Ronald W., and Emma Jincheng Zhang. "How valuable are independent directors? Evidence from external distractions." Journal of Financial Economics (2018).海外文献推荐-第七十三期参考文献:[1] Hunter D, Kandel E, Kandel S, et al. Mutual fund performance evaluation with active peer benchmarks[J]. Journal of Financial economics, 2014, 112(1): 1-29.海外文献推荐-第七十四期参考文献:[1]Michael Stein and Svetlozar T. Rachev. Style Neutral Funds of Funds: Diversification or Deadweight? [J]. Journal of Asset Management, February 2011, V olume 11, Issue 6, pp 417–434海外文献推荐-第七十五期参考文献:[1] Elisabeth Kashner, 2019.01.31, Bogle led this investing Fee War, ;[2] Cinthia Murphy,2017,03.31, how to launch a successful ETF, ;[3] Drew V oros, 2019.01.23, how a small ETF Issuer Competes, ;[4] 2019.01.04, Invesco focusing on scale,海外文献推荐-第七十六期参考文献:[1] Shpak I , Human B , Nardon A . Idiosyncratic momentum in commodity futures[J]. Social Science Electronic Publishing, 2017.海外文献推荐-第七十六期参考文献:[1] Ehsani S , Linnainmaa J T . Factor Momentum and the Momentum Factor[J]. Social Science Electronic Publishing, 2017.海外文献推荐-第七十七期参考文献:[1] Iuliia Shpak*, Ben Human and Andrea Nardon. 2017.09.11, Idiosyncratic momentum in commodity futures. ResearchGate海外文献推荐-第七十八期参考文献:[1] Joel Hasbrouck. High-Frequency Quoting: Short-Term V olatility in Bids and Offers. JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS海外文献推荐-第七十九期参考文献:[1] Tarun Gupta and Bryan Kelly. Factor Momentum Everywhere. Institutional Investor Journals海外文献推荐-第八十期参考文献:[1] MICHAEL A. BABYAK , P H D. What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models. S T A T I S T I C A L C O R N E R海外文献推荐-第八十一期参考文献:[1] Eric Jondeau , Qunzi Zhang , Xiaoneng Zhu. Average Skewness Matters.海外文献推荐-第八十二期参考文献:[1] JOHN A. HASLEM. Morningstar Mutual Fund Measures and Selection Model. THE JOURNAL OF WEALTH MANAGEMENT海外文献推荐-第八十三期参考文献:[1] EUGENE F. FAMA and KENNETH R. FRENCH. Luck versus Skill in the Cross-Section of Mutual Fund Returns. THE JOURNAL OF FINANCE海外文献推荐-第八十四期参考文献:[1] How Transparent Are ETFs?[2] Lara Crigger. Nontransparent Active: Next ETF Revolution?.海外文献推荐-第八十五期参考文献:[1] Olivier Rousse and Benoît Sévi. Informed Trading in Oil-Futures Market. Fondazione Eni Enrico Mattei (FEEM)海外文献推荐-第八十六期参考文献:[1] Ari Levine and Lasse Heje Pedersen. Which Trend is Your Friend?。
索尼PEST分析
熊婷——索尼是世界视听、电子游戏、通讯产品和信息技术等领域的先导者。
是世界最早便携式数码产品的开创者,是世界最大的电子产品制造商之一、世界电子游戏业三大巨头之一。
索尼也是全世界最大的电影公司和世界上最大的音乐公司。
鉴于它是综合性的跨国集团,我们仅以索尼中国有限公司为例,运用PEST模型分析。
夏奎——政治环境:我们正处于第十二个五年规划时期,“十二五”时期是全面建设小康社会的关键时期,是深化改革开放、加快转变经济发展方式的攻坚时期。
国家坚持把科技进步和创新作为加快转变经济发展方式的重要支撑,并加强自主品牌的研发。
在《中共中央关于制定国民经济和社会发展第十二个五年规划的建议》明确提出我们要发展现代产业体系,提高产业核心竞争力,增强科技创新能力。
在《电子信息制造业“十二五”发展规划》中提到:电子信息产业仍是全球竞争的战略重点、融合创新推动产业格局发生重大变革、国内外市场环境机遇与挑战并存。
上述情况表明我国需要引导优势资源向优势企业集中,在于国外竞争力强、知名度高的企业保持良好的合作情况下,提高我们民主品牌的竞争力,为全面建设小康社会发挥带头作用。
无论是索尼还是苹果、三星,面临的挑战都是一样:必须不断提高自身核心竞争力,对抗天朝大力扶持的日益壮大的民主品牌。
娜娜——经济环境:近年,全球经济风起云涌,随着欧洲主权债务危机的蔓延和美国经济陷入高失业、高负债的困境,世界经济复苏的不稳定性、不确定性明显上升。
国内虽然经济仍持续增长,但增长速度逐渐回落。
稳中求进不仅是国家也是多数企业的做法,国家正通过新一代信息技术的发展,促进各行业发展。
中国作为发展中的强国,不仅是索尼,其它世界巨头也纷纷把市场重心转移到中国。
作为开发者和制造者,索尼不同的产品都面临不同强力竞争的对手。
Android的开源,使智能手机和平板电脑等终端竞争更加激烈。
与此同时,从2007年,索尼财政已经扭转了之前一直巨额亏损的窘境。
近年,索尼大多数部门均实现盈利,到2012年已摆脱多年一直亏损的状态,这无疑是对索尼发展战略的肯定。
爱普生打印机 网络接口说明ube02trm_e_1
CE Marking
The board conforms to the following Directives and Norms: Directive 89/336/EEC EN 55022 Class B EN 55024 IEC 61000-4-2 IEC 61000-4-3 IEC 61000-4-4 IEC 61000-4-5 IEC 61000-4-6 IEC 61000-4-11 EN45501
WARNING
The connection of a non-shielded interface cable to this board will invalidate the EMC standards of this device. You are cautioned that changes or modifications not expressly approved by Seiko Epson Corporation could void your authority to operate the equipment.
Related Documents
Software/document name UB-E02 User’s Manual Description Provides instructions for operators of POS systems in which the UB-E02 is installed so that the operators can use the UB-E02 safely and correctly.
Rev. A
i
Revision Information
Revision Rev. A Page Altered Items and Contents
SONY MDS-E11、MDS-E53、MDS、E58操作说明书
• 中继录音/放音电缆(例如RK—G13b)
(立体声小型jack) 连接 • 录音器连接到模拟类装置
基本操作/Basic operations 在MD上录音
模拟输入
模拟输入时,后面板上的ANALOG INPUT(平衡或不平衡)开关接输入端子的
型号使用。
录音期间的音频监听
即使是设置在单声道录音模式,监听信号也不会变为单声道音频。
2、在要开始录音处按 键。
3、继续从“在MS上录音”第1步进行。
注:在“PROGRAM ”或“SHUFFLE”播放期间不能从轨迹中间录音。
调整录音电平
当INPUT输入设置到ANALOG(模拟)时录音,信号输入通过LINE
(ANALOG)IN插口,开始录音前,用REC LEVEL 调整录音电平。
数码录音期间,不能调整电平。
SONY MDS-E11 操作说明书(共32页)
SONY MDS-E11、MDS-E53、MDS、E58操作说明书
开始 GOTING STARTED
开箱
检查你收到的下列附件:
• 遥控器RM-D7M 1个
• Rb电池(AA尺寸) 2个
把电池装入遥控器 插图;遥控器
装入2个Rb电池时,注意对准电池的+、- 极。
录音时标志轨迹号码
可以用人工,或自动标志轨迹号码,在规定的位置标志号码可以在以后使用
AMS功能或编辑功能时得到快速的定位位置。(插图)
人工标志轨迹号码(人工轨迹标志)
在MD上录音时,可在任何时间标志轨迹号码。录音时要增加轨迹标志,
可以按 ●。
自动标志轨迹号码(自动轨迹标志)
在下列情况,录音器增加不同的轨迹标志:
记号码(即从不同的MD或CD录制的信号)播放,单轨迹或多轨迹作为单个信 号录音,则用一个单轨迹号码,如果信号尖是一具MD,小于4秒的轨迹可以不 打标记号码。 • 当INPUT设置到DIGITAL ,从DAT或卫星广播录音时,无论何时,输入信
MIMO (英文文献)
Paper approved by N. C. Beaulieu, the Editor for Wireless Communication Theory of the IEEE Communications Society. Manuscript received February 23, 2006; revised August 1, 2006. This work was supported by National Natural Science Foundation of China under Grants 60572072 and 60496311, the China High-Tech 863 Plan under Grant 2006AA01Z264, National Basic Research Program of China under Grant 2007CB310603, and the Doctor Subject Foundation of China under Grant 20060286016.
M. R. McKay is with the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. (email: eemckay@ust.hk).
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Joint effect ofbrand and country image
5
Measuring the joint effect ofbrand and country image inconsumer evaluation of globalproducts
Israel D. Nebenzahl and Eugene D. JaffeBar-Ilan University, Israel
The decision to enter a foreign market depends on the consideration of a largeset of economic, political and cultural variables. The purpose of the investmentis also an important determining factor (Root, 1987). Typical treatment of thesevariables found in the international business literature focuses on supply sideconsiderations, e.g. alternative labour and transportation costs, border taxes,etc. In this paper, we focus instead on consumer perception, and consider how itis affected by cross-border shifts of production.The purpose of this paper is to measure how perception of brand imagechanges as production is sourced multinationally. In spite of the fact thatmultinational corporations produce and assemble products bearing identicalbrand names (e.g. IBM, Pierre Cardin, General Foods, Henkel, Vicks, Black &Decker) in both developed and developing countries, little research has beencarried out to measure the effect of host country location on brand image. Thelack of research in this regard is all the more surprising because of findings thatconsumer evaluation of products is influenced by a country’s stage ofdevelopment, i.e. consumers hold more negative perceptions of products madein developing countries (Wang and Lamb, 1983) and that the sourcing country(Han and Terpstra, 1988) and country of origin (Tse and Gorn, 1992) havegreater effects on consumer evaluations of product quality than does brandname. Moreover, research of brand image and brand equity management(Aaker and Keller, 1990; Keller, 1993; Park et al., 1991) have shown that brandimage strategies should be determined before other elements of the marketingmix. Finally, there is some evidence that country of origin has been concealed inorder to prevent loss of sales. For example, many Chrysler dealers in the South-West USA were either refusing delivery of Dodge and Plymouth K cars made inMexico, or were tearing off stickers that indicated country of origin. ManyIsraeli consumers refused to buy Volkswagen cars made in Brazil when it wasknown that identical models were available from Germany, albeit at a higherprice.
The authors gratefully acknowledge the helpful comments and critique of the journal reviewers.Both authors contributed equally to the paper.International Marketing Review,
Vol. 13 No. 4, 1996, pp. 5-22.© MCBUniversity Press, 0265-1335InternationalMarketing Review13,4
6
Past studies on the subject of foreign sourcing were limited to consideringchanges in brand image, without measuring the image dimensions asproduction is shifted globally. Moreover, most of these studies haveconcentrated on the automobile industry (Han and Terpstra, 1988; Johanssonand Nebenzahl, 1986; Johansson and Thorelli, 1985; Johansson et al., 1985;Stewart and Chan, 1993). Johansson and Nebenzahl (1986) found that sourcingthe production of Honda and Mazda automobiles in South Korea, Mexico or thePhilippines would detract considerably from brand attractiveness compared toproduction in Japan. A replication of this study by Stewart and Chan (1993),using tourist coaches as the product category studied, found that Mercedes-Benz buses made in Brazil and South Korea had a significantly lower imagewhen compared to production in their home country. Han and Terpstra (1988)also found that the brand image of automobiles made in the USA and Japaneroded when production was shifted to South Korea. The studies cited above provide conclusive evidence that the product valuegenerated by global brand names may not outweigh the effect of country imagewhen production is sourced to less developed countries. The conclusion is thata global manufacturer should concentrate production in developed countries oradopt countervailing strategies, such as emphasize the German origin ofautomobiles even though they are made in Brazil (Han and Terpstra, 1988), usea “neutral” brand when producing in a low-image country (Tse and Gorn, 1992),or discount the product price (Johansson and Nebenzahl, 1986). All of thesesuggested strategies apply across low-image countries. However, there may bea case for the proposition that the perceptual dimensions on which products areperceived vary by country and that marketing strategies should be country-specific. For example, a product made in Mexico and in South Korea may beperceived quite differently by US consumers when sold under the same globalbrand name. If so, the way to treat this difference may be embedded in thespecific construct of brand image of each country. Up to now, the existence ofsuch constructs has not been explored fully. Johansson and Nebenzahl (1986)and Stewart and Chan (1993) factor analysed and mapped the image ofcountries by brand. However, they did not determine the dimensions of eachbrand-country combination. We attempt to fill this gap in the literature bydemonstrating how changes in constructs of brand image as production isshifted across countries can be assessed and what are the implications of suchshifts for marketing strategy. Two shifts are the subject of this study. First, ashift from one developed country to another, i.e. from Japan to the USA and viceversa. Second, a shift from developed to developing countries, i.e. from Japanand the USA to Hungary, Poland and Russia.The use of individual brand-country factor analyses, as opposed to meanratings, enables the focusing of attention on dimensional differences (orsimilarities), rather than on individual product attributes or overall ratings.Thus, consumers’ perceptions of brand value are defined by image dimensions.Once brand values are determined, marketing strategy may be formulated. Forexample, if image dimensions show little change as products are sourced across