메뉴 건너뛰기




Volumn 65, Issue 1, 2000, Pages 93-119

Bayesian inference for finite mixtures of generalized linear models with random effects

Author keywords

Bayesian inference; Consumer behavior; Finite mixtures; Generalized linear models; Heterogeneity; Latent class analysis; Markov chain Monte Carlo

Indexed keywords


EID: 0034147039     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02294188     Document Type: Article
Times cited : (127)

References (55)
  • 1
    • 0000501656 scopus 로고
    • Information theory and an extension of the maximum likelihood principle
    • N. Petrov & F. Csadki (Eds.), Budapest: Akademiai Kiado
    • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In N. Petrov & F. Csadki (Eds.), Proceedings of the Second International Symposium of Information Theory (pp. 267-281). Budapest: Akademiai Kiado.
    • (1973) Proceedings of the Second International Symposium of Information Theory , pp. 267-281
    • Akaike, H.1
  • 2
  • 4
    • 34250108028 scopus 로고
    • Model selection and Akaike's information criterion (AIC): The general theory and its analytical extension
    • Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extension. Psychometrika, 52, 345-370.
    • (1987) Psychometrika , vol.52 , pp. 345-370
    • Bozdogan, H.1
  • 8
    • 32344446687 scopus 로고
    • Understanding the metropolis-hastings algorithm
    • Chib, S., & Greenberg, E. (1995). Understanding the Metropolis-Hastings algorithm. The American Statistician, 49, 327-335.
    • (1995) The American Statistician , vol.49 , pp. 327-335
    • Chib, S.1    Greenberg, E.2
  • 9
  • 12
    • 0001237218 scopus 로고
    • A maximum likelihood methodology for clusterwise linear regression
    • DeSarbo, W. S., & Cron W. L. (1988). A maximum likelihood methodology for clusterwise linear regression. Journal of Classification, 5, 248-282.
    • (1988) Journal of Classification , vol.5 , pp. 248-282
    • DeSarbo, W.S.1    Cron, W.L.2
  • 14
    • 21144468655 scopus 로고
    • A latent pooling methodology for regression analysis with limited time series of cross sections: A PIMS data application
    • DeSarbo, W. S., Ramaswamy, V., Reibstein, D. J., & Robinson, W. T. (1993). A latent pooling methodology for regression analysis with limited time series of cross sections: A PIMS data application. Marketing Science, 12, 103-124.
    • (1993) Marketing Science , vol.12 , pp. 103-124
    • DeSarbo, W.S.1    Ramaswamy, V.2    Reibstein, D.J.3    Robinson, W.T.4
  • 15
    • 0002067023 scopus 로고
    • A latent class probit model for analyzing pick any/n data
    • De Soete, G., & DeSarbo, W. S. (1991). A latent class probit model for analyzing pick any/n data. Journal of Classification, 8, 45-63.
    • (1991) Journal of Classification , vol.8 , pp. 45-63
    • De Soete, G.1    DeSarbo, W.S.2
  • 19
    • 0000954353 scopus 로고    scopus 로고
    • Efficient metropolis jumping rules
    • J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. M. F. Smith (Eds.), Cambridge: Oxford University Press
    • Gelman, A., Roberts, G. O., & Gilks, W. R. (1996). Efficient metropolis jumping rules. In J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. M. F. Smith (Eds.), Bayesian statistics 5 (pp. 599-607). Cambridge: Oxford University Press.
    • (1996) Bayesian Statistics , vol.5 , pp. 599-607
    • Gelman, A.1    Roberts, G.O.2    Gilks, W.R.3
  • 20
    • 84972492387 scopus 로고
    • Iterative simulation using single and multiple sequences
    • Gelman, A., & Rubin, D. B. (1992). Iterative simulation using single and multiple sequences. Statistical Science, 7, 457-511.
    • (1992) Statistical Science , vol.7 , pp. 457-511
    • Gelman, A.1    Rubin, D.B.2
  • 21
    • 84972511893 scopus 로고
    • Practical Markov chain Monte Carlo
    • Geyer, C. J. (1992). Practical Markov chain Monte Carlo. Statistical Science, 7, 473-511.
    • (1992) Statistical Science , vol.7 , pp. 473-511
    • Geyer, C.J.1
  • 22
    • 85041975304 scopus 로고
    • Exploratory latent structure analysis using both identified and unidentified models
    • Goodman, L. A. (1974). Exploratory latent structure analysis using both identified and unidentified models. Biometrika, 61, 215-231.
    • (1974) Biometrika , vol.61 , pp. 215-231
    • Goodman, L.A.1
  • 23
    • 77956890234 scopus 로고
    • Monte Carlo sampling methods using Markov chains and their applications
    • Hastings, W. K (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 24
    • 84963154792 scopus 로고
    • Maximum likelihood estimates of the parameters of a mixture of two regression lines
    • Hosmer, D. W. (1974). Maximum likelihood estimates of the parameters of a mixture of two regression lines. Communications in Statistics, Series B, 3, 995-1006.
    • (1974) Communications in Statistics, Series B , vol.3 , pp. 995-1006
    • Hosmer, D.W.1
  • 26
    • 84985569561 scopus 로고
    • Fitting finite mixture models in a regression context
    • Jones, P. N., & McLachlan, G. J. (1992). Fitting finite mixture models in a regression context. Australian Journal of Statistics, 43, 233-240.
    • (1992) Australian Journal of Statistics , vol.43 , pp. 233-240
    • Jones, P.N.1    McLachlan, G.J.2
  • 27
    • 0002035815 scopus 로고
    • Estimating flexible distributions of ideal-points with external analysis of preference
    • Kamakura, W. A. (1991). Estimating flexible distributions of ideal-points with external analysis of preference. Psychometrika, 56, 419-448.
    • (1991) Psychometrika , vol.56 , pp. 419-448
    • Kamakura, W.A.1
  • 28
    • 0000917415 scopus 로고
    • A probabilistic choice model for market segmentation and elasticity structure
    • Kamakura, W. A., & Russell, G. J. (1989). A probabilistic choice model for market segmentation and elasticity structure. Journal of Marketing Research, 26, 379-390.
    • (1989) Journal of Marketing Research , vol.26 , pp. 379-390
    • Kamakura, W.A.1    Russell, G.J.2
  • 30
    • 0030521288 scopus 로고    scopus 로고
    • Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs
    • Lenk, P. J., DeSarbo, W. S., Green, P. E., & Young, M. R. (1996). Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Science, 15(2), 173-191.
    • (1996) Marketing Science , vol.15 , Issue.2 , pp. 173-191
    • Lenk, P.J.1    DeSarbo, W.S.2    Green, P.E.3    Young, M.R.4
  • 31
    • 0031514994 scopus 로고    scopus 로고
    • Estimating Bayes factors via posterior simulation with the Laplace - Metropolis estimator
    • Lewis, S. M. & Raftery, A. E. (1997). Estimating Bayes factors via posterior simulation with the Laplace - Metropolis estimator. Journal of American Statistical Association, 92, 648-655.
    • (1997) Journal of American Statistical Association , vol.92 , pp. 648-655
    • Lewis, S.M.1    Raftery, A.E.2
  • 33
    • 0024475559 scopus 로고
    • Probits of mixtures
    • Lwin, T., & Martin, P. J. (1989). Probits of mixtures. Biometrics, 45, 721-732.
    • (1989) Biometrics , vol.45 , pp. 721-732
    • Lwin, T.1    Martin, P.J.2
  • 35
    • 0002297105 scopus 로고
    • Conditional logit analysis of qualitative choice behavior
    • P. Zarembda (Ed.), New York: Academic Press
    • McFadden, D. (1974) Conditional logit analysis of qualitative choice behavior. In P. Zarembda (Ed.), frontiers in econometrics (pp. 105-142). New York: Academic Press.
    • (1974) Frontiers in Econometrics , pp. 105-142
    • McFadden, D.1
  • 36
    • 0001371071 scopus 로고
    • A generalized theory of the combination of observations so as to obtain the best result
    • Newcomb, S. (1886). A generalized theory of the combination of observations so as to obtain the best result. American Journal of Mathematics, Series B, 8, 343-366.
    • (1886) American Journal of Mathematics, Series B , vol.8 , pp. 343-366
    • Newcomb, S.1
  • 37
    • 0002100868 scopus 로고
    • Contribution to the mathematical theory of evolution
    • Pearson, K. (1894). Contribution to the mathematical theory of evolution. Philosophical Transactions, Series A, 185, 71-110.
    • (1894) Philosophical Transactions, Series A , vol.185 , pp. 71-110
    • Pearson, K.1
  • 38
    • 0000094543 scopus 로고    scopus 로고
    • Convergence of Markov chain Monte Carlo algorithms
    • J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. M. F. Smith (Eds.), Cambridge: Oxford University Press
    • Poison, Nicholas G. (1996). Convergence of Markov chain Monte Carlo algorithms. In J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. M. F. Smith (Eds.), Bayesian statistics 5 (pp. 297-312). Cambridge: Oxford University Press.
    • (1996) Bayesian Statistics , vol.5 , pp. 297-312
    • Poison, N.G.1
  • 39
  • 40
    • 84950321853 scopus 로고
    • Estimating mixtures of normal distributions and switching regression
    • Quandt, R. E., & Ramsey, J. B. (1978). Estimating mixtures of normal distributions and switching regression. Journal of American Statistical Association, 73, 730-738.
    • (1978) Journal of American Statistical Association , vol.73 , pp. 730-738
    • Quandt, R.E.1    Ramsey, J.B.2
  • 43
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
    • (1978) Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 44
    • 0003053548 scopus 로고
    • Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
    • Smith, A. F. M., & Roberts, G. O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 55, 3-23.
    • (1993) Journal of the Royal Statistical Society, Series B , vol.55 , pp. 3-23
    • Smith, A.F.M.1    Roberts, G.O.2
  • 47
    • 21844487977 scopus 로고
    • Computing Bayes factors using a generalization of the Savage-Dickey density ratio
    • Verdinelli, I., & Wasserman, L. (1995). Computing Bayes factors using a generalization of the Savage-Dickey density ratio. Journal of American Statistical Association, 90, 614-618.
    • (1995) Journal of American Statistical Association , vol.90 , pp. 614-618
    • Verdinelli, I.1    Wasserman, L.2
  • 50
    • 0029938342 scopus 로고    scopus 로고
    • Mixed Poisson regression with covariate dependent rates
    • Wang, P. M., Puterman, M. L., Le, N., & Cockburn, I. (1996). Mixed Poisson regression with covariate dependent rates. Biometrics, 52, 381-400.
    • (1996) Biometrics , vol.52 , pp. 381-400
    • Wang, P.M.1    Puterman, M.L.2    Le, N.3    Cockburn, I.4
  • 51
    • 84990623085 scopus 로고
    • A latent class binomial logit methodology for the analysis of paired comparison data: An application reinvestigating the determinants of perceived risk
    • Wedel, M., & DeSarbo, W. S. (1993). A latent class binomial logit methodology for the analysis of paired comparison data: An application reinvestigating the determinants of perceived risk. Decision Science, 24, 1157-1170.
    • (1993) Decision Science , vol.24 , pp. 1157-1170
    • Wedel, M.1    DeSarbo, W.S.2
  • 52
    • 0002317299 scopus 로고
    • A review of recent developments in latent structure regression models
    • R. Bagozzi (Ed.), London: Blackwell Publishing
    • Wedel, M., & DeSarbo, W. S. (1994). A Review of recent developments in latent structure regression models. In R. Bagozzi (Ed.), Advanced methods of marketing research (pp. 352-388), London: Blackwell Publishing.
    • (1994) Advanced Methods of Marketing Research , pp. 352-388
    • Wedel, M.1    DeSarbo, W.S.2
  • 53
    • 34249753047 scopus 로고
    • A mixture likelihood approach for generalized linear models
    • Wedel, M., & DeSarbo, W. S. (1995). A mixture likelihood approach for generalized linear models. Journal of Classification, 12, 21-55.
    • (1995) Journal of Classification , vol.12 , pp. 21-55
    • Wedel, M.1    DeSarbo, W.S.2
  • 54
    • 84986413084 scopus 로고
    • A latent class Poisson regression model for heterogeneous count data with an application to direct mail
    • Wedel, M., DeSarbo, W. S., Bult, J. R., & Ramaswamy, V. (1993). A latent class Poisson regression model for heterogeneous count data with an application to direct mail. Journal of Applied Econometrics, 8, 397-411.
    • (1993) Journal of Applied Econometrics , vol.8 , pp. 397-411
    • Wedel, M.1    DeSarbo, W.S.2    Bult, J.R.3    Ramaswamy, V.4
  • 55
    • 84910906566 scopus 로고
    • Generalized linear models with random effects: A Gibbs sampling approach
    • Zeger, S. L., & Karim, M. R. (1991). Generalized linear models with random effects: A Gibbs sampling approach. Journal of American Statistical Association, 86, 79-679.
    • (1991) Journal of American Statistical Association , vol.86 , pp. 79-679
    • Zeger, S.L.1    Karim, M.R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.