A Heterogeneous Bayesian Regression Model for Cross-sectional Data Involving a Single Observation per Response Unit

被引：0
作者：
Fong D.K.H. [1 ]
Ebbes P. [2 ]
DeSarbo W.S. [1 ]
机构：
[1] Marketing Department, Smeal College of Business, Pennsylvania State University, University Park, PA 16802
[2] Fisher College of Business, The Ohio State University, Columbus
来源：
Psychometrika | 2012年 / 77卷 / 2期
关键词：
Bayesian estimation; consumer psychology; cross-sectional analysis; heterogeneity;
D O I：
10.1007/s11336-012-9252-x
中图分类号：
学科分类号：
摘要：
Multiple regression is frequently used across the various social sciences to analyze cross-sectional data. However, it can often times be challenging to justify the assumption of common regression coefficients across all respondents. This manuscript presents a heterogeneous Bayesian regression model that enables the estimation of individual-level-regression coefficients in cross-sectional data involving a single observation per response unit. A Gibbs sampling algorithm is developed to implement the proposed Bayesian methodology. A Monte Carlo simulation study is constructed to assess the performance of the proposed methodology across a number of experimental factors. We then apply the proposed method to analyze data collected from a consumer psychology study that examines the differential importance of price and quality in determining perceived value evaluations. © 2012 The Psychometric Society.
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页码：293 / 314
页数：21
相关论文
共 37 条
  • [1] Addelman S., Irregular fractions of the 2 <sup>n</sup> factorial experiments, Technometrics, 3, pp. 479-496, (1961)
  • [2] Allenby G.M., Ginter J.L., Using extremes to design products & segment markets, Journal of Marketing Research, 32, pp. 392-403, (1995)
  • [3] Ansari A., Jedidi K., Heterogeneous factor analysis models: a Bayesian approach, Psychometrika, 67, pp. 49-78, (2002)
  • [4] Bayarri M.J., Berger J.O., P values for composite null models, Journal of the American Statistical Association, 95, pp. 1127-1142, (2000)
  • [5] Bayarri M.J., Berger J.O., The interplay of Bayesian and frequentist analysis, Statistical Science, 19, pp. 58-80, (2004)
  • [6] Bayarri M.J., Castellanos M.E., Bayesian checking of the second levels of hierarchical models, Statistical Science, 22, pp. 322-343, (2007)
  • [7] Berger J.O., Statistical Decision Theory and Bayesian Analysis, (1985)
  • [8] Berger J.O., Bayesian analysis: a look at today and thoughts of tomorrow, Journal of the American Statistical Association, 95, pp. 1269-1276, (2000)
  • [9] Berger J.O., Strawderman W., Tang D., Posterior propriety and admissibility of hyperpriors in normal hierarchical models, Annals of Statistics, 33, pp. 606-646, (2005)
  • [10] Bernardo J.M., Smith A.F.M., Bayesian Theory, (1994)
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