A Hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models
Published Online:1 Nov 2000https://doi.org/10.1287/mksc.19.4.328.11789
References
- Using extremes to design products and segment markets. J. Marketing Res. (1995) 32:392–403Crossref, Google Scholar
- , Wansbeek T., Wedel M. Marketing models of consumer heterogeneity. J. Econometrics (1999) 89(1-2):57–78Special Issue: Marketing and EconometricsGoogle Scholar
- Bayesian factor analysis for multilevel binary observations. Psychometrika (2000) . forthcomingCrossref, Google Scholar
- A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hastings algorithm. Psychometrika (1998) 63:271–300Crossref, Google Scholar
- CODA: Convergence Diagnostics and Output Analysis Software for Gibbs Sampler Output: Version 0.3 (1995) . Technical report, Biostatistics Unit-MRC, Cambridge, U.KGoogle Scholar
- Structural Equations with Latent Variables (1989) (Wiley Interscience, New York) Crossref, Google Scholar
- Understanding the Metropolis-Hastings algorithm. Amer. Statist. (1995) 49:327–335Google Scholar
- Investigating heterogeneity in brand preferences in logit models for panel data. J. Marketing Res. (1991) 28:417–428Crossref, Google Scholar
- , Gilks W. R., Richardson S., Spiegelhalter D. J. Model determination using sampling-based methods. Markov Chain Monte Carlo in Practice (1996) (Chapman and Hall, London) 145–161Crossref, Google Scholar
- Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc. (1990) 85:972–985Crossref, Google Scholar
- Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intelligence (1984) 6:721–741Crossref, Google Scholar
- A general model for the analysis of multilevel data. Psychometrika (1988) 553:455–467Crossref, Google Scholar
- Monte Carlo sampling methods using Markov chains and their applications. Biometrika (1970) 57:97–109Crossref, Google Scholar
- . Marketing Strategy and Uncertainty (1999) (Oxford University Press, New York) Google Scholar
- Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Sci. (1997) 16(1):39–59Link, Google Scholar
- Rational and adaptive performance expectations in a customer satisfaction framework. J. Consumer Res. (1995) 21:695–707Crossref, Google Scholar
- Simultaneous factor analysis in several populations. Psychometrika (1971) 36:409–426Crossref, Google Scholar
- A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. (1989) 26(November):379–390Crossref, Google Scholar
- A Bayesian approach to confirmatory factor analysis. Psychometrika (1981) 46:153–160Crossref, Google Scholar
- Factor analysis for clustered observations. Psychometrika (1992) 57:581–597Crossref, Google Scholar
- Statistical Theories of Mental Test Scores (1968) (Addison-Wesley, Reading, MA) 129–131Google Scholar
- Equations of state calculations by fast computing machines. J. Chem. Phys. (1953) 21:1087–1091Crossref, Google Scholar
- Latent variable modeling in heterogeneous populations. Psychometrika (1989) 54:557–585Crossref, Google Scholar
- Multilevel covariance structure analysis. Sociol. Methods Res. (1994) 22:376–398Crossref, Google Scholar
- Approximate Bayesian inference by the weighted likelihood bootsrap (with discussion). J. Roy. Statis. Soc. Ser. B (1994) 56:3–18Google Scholar
- Cognitive, affective, and attribute bases of the satisfaction response. J. Consumer Res. (1993) 20:418–430Crossref, Google Scholar
- Satisfaction: A Behavioral Perspective on the Consumer (1997) (McGraw-Hill, New York) Google Scholar
- Overcoming scale heterogeneity. (1999) . Working paper, University of Chicago, Chicago, ILGoogle Scholar
- Bayesian estimation and testing of structural equation models. Psychometrika (1999) 64:37–52Crossref, Google Scholar
- , Joreskog K. G., Wold H. Structural equation models with structured means. Systems Under Indirect Observation: Causality, Structure, And Prediction (1981) (North Holland, Amsterdam, Netherlands)183–195Google Scholar
- The calculation of posterior distributions by data augmentation (with Discussion). J. Amer. Statist. Assoc. (1987) 82:528–550Crossref, Google Scholar
- . Finite mixtures in confirmatory factor-analysis models. Psychometrika (1997) 62(3):297–330Crossref, Google Scholar
- . An Introduction to Bayesian Inference in Econometrics (1971) (Wiley, New York) Google Scholar

