A Finite Mixture Logit Model to Segment and Predict Electronic Payments System Adoption

2011 ◽  
Vol 22 (1) ◽  
pp. 118-133 ◽  
Author(s):  
Ravi Bapna ◽  
Paulo Goes ◽  
Kwok Kee Wei ◽  
Zhongju Zhang
Keyword(s):  
Logit Model ◽  
Finite Mixture ◽  
Electronic Payments ◽  
Payments System

A Finite Mixture Logit Model of Recreational Angling with Serially Correlated Random Utility

2002 ◽  
Vol 84 (4) ◽  
pp. 1066-1075 ◽  
Author(s):  
Bill Provencher ◽  
Kenneth A. Baerenklau ◽  
Richard C. Bishop
Keyword(s):  
Logit Model ◽  
Finite Mixture ◽  
Random Utility ◽  
Recreational Angling

scholarly journals Bayesian Inference in the Multinomial Logit Model

2016 ◽  
Vol 41 (1) ◽  
Author(s):  
Sylvia Frühwirth-Schnatter ◽  
Rudolf Frühwirth
Keyword(s):  
Bayesian Inference ◽  
Logit Model ◽  
Latent Variable ◽  
Random Variables ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Finite Mixture ◽  
Logistic Distribution ◽  
Mnl Model ◽  
Variable Representation

The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.

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