A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application. [PDF]
PLoS ONE, 2017 Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling.
Yongsheng Zhang +3 more
doaj +5 more sources
Fast Variational Bayes Methods for Multinomial Probit Models [PDF]
Journal of Business & Economic Statistics, 2022 The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets.
Rubén Loaiza‐Maya, Didier Nibbering
semanticscholar +5 more sources
Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects. [PDF]
Stat Med, 2016 Warren JL +10 more
europepmc +4 more sources
Mixed Multinomial Probit Model Accommodating Flexible Covariance Structure and Random Taste Variation: An Application to Commute Mode Choice Behavior [PDF]
Journal of Advanced Transportation, 2022 This paper developed a mixed multinomial probit (MMNP) model with alternative error specification and random coefficients (for both generic variables and personal attributes) to accommodate flexible covariance structure and taste variation.
Ke Wang, Xin Ye, Hongcheng Gan
doaj +2 more sources
Scalable Bayesian estimation in the multinomial probit model [PDF]
Journal of business & economic statistics, 2020 The multinomial probit (MNP) model is a popular tool for analyzing choice behavior as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice ...
Rubén Loaiza‐Maya, Didier Nibbering
openalex +3 more sources
Bayesian Conjugacy in Probit, Tobit, Multinomial Probit and Extensions: A Review and New Results [PDF]
Journal of the American Statistical Association, 2023 A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including classical Gaussian response settings, this class also encompasses probit, multinomial probit
Niccolò Anceschi +3 more
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Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees. [PDF]
J Comput Graph StatThe multinomial probit (MNP) framework is based on a multivariate Gaussian latent structure, allowing for natural extensions to multilevel modeling. Unlike multinomial logistic models, MNP does not assume independent alternatives.
Xu Y +4 more
europepmc +2 more sources
A Symmetric Prior for Multinomial Probit Models [PDF]
, 2020 Fitted probabilities from widely used Bayesian multinomial probit models can depend strongly on the choice of a base category, which is used to uniquely identify the parameters of the model.
Lane F. Burgette +2 more
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Adapting Agriculture to Climate Change: Are Climate-Smart Practices Important in Burkina Faso? [PDF]
Plant Environ InteractABSTRACT In Burkina Faso, smallholder farmers rely heavily on rain‐fed agriculture, which is affected by climate change. The adoption of climate‐smart practices is essential to strengthen the resilience of agricultural systems to climate change and improve household food security and, consequently, global food security.
Bado B, Thiombiano N, Tito NT.
europepmc +2 more sources
A Multinomial Ordinal Probit Model with Singular Value Decomposition Method for a Multinomial Trait [PDF]
Journal of Probability and Statistics, 2012 We developed a multinomial ordinal probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms (SNPs) simultaneously for association with multidisease status when sample size is much smaller than the ...
Soonil Kwon +7 more
doaj +2 more sources