A Bayesian mixed logit-probit model for multinomial choice

Journal of Econometrics

Volumn 147, Issue 2, 2008, Pages 232-246

  Burda, Martin ^a   Harding, Matthew ^b   Hausman, Jerry ^c  

^a UNIVERSITY OF TORONTO   (Canada)

^b STANFORD UNIVERSITY   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Dirichlet process prior; Hierarchical latent class models; Multinomial discrete choice model; Non conjugate priors

Indexed keywords

BAYESIAN NETWORKS; DATA STRUCTURES; DYNAMIC POSITIONING; FILE ORGANIZATION; LEARNING ALGORITHMS; MARKOV PROCESSES; MONTE CARLO METHODS; NORMAL DISTRIBUTION; PERCOLATION (SOLID STATE); RETAIL STORES; SPECIFICATIONS; TREES (MATHEMATICS);

BAYESIAN; BAYESIAN MARKOV CHAIN MONTE CARLO; BREAK DOWNS; COMPLEX DATA STRUCTURES; DIRICHLET PROCESS PRIOR; DIRICHLET PROCESSES; DISCRETE CHOICES; DRIVING DISTANCES; GENERAL CLASSES; HIERARCHICAL LATENT CLASS MODELS; HOUSTON; INDEPENDENCE OF IRRELEVANT ALTERNATIVES; KEY VARIABLES; LATENT CLASSES; MIXED LOGIT; MIXED MODELS; MULTI MODALS; MULTINOMIAL; MULTINOMIAL DISCRETE CHOICE MODEL; MULTIVARIATE NORMAL DISTRIBUTIONS; NON-CONJUGATE PRIORS; NONPARAMETRIC; ORIGINAL SYSTEMS; PARAMETRIC APPROACHES; PROBIT MODELS; SAMPLING ALGORITHMS; SCANNER DATUMS; SHOPPING BEHAVIORS;

PUBLIC KEY CRYPTOGRAPHY;

EID: 55949126156     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2008.09.029     Document Type: Article

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