Results 181 to 190 of about 4,588 (212)

A limited information estimator for the multivariate ordinal probit model

Applied Economics, 2000
A limited information estimator for the multivariate ordinal probit model is developed. The main advantage of the estimator is that even for high dimensional models, the estimation procedure requires the evaluation of bivariate normal integrals only. The proposed estimator also avoids the potential problem of encountering local maxima in the estimation
Fu, Tsu-Tan, Li, Lung-An, Lin, Yih-Ming, Kan, Kamhon   +3 more
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Bayesian Analysis of Multivariate Probit Models [PDF]

, 1996
This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Markov chain Monte Carlo version of the E-M algorithm.
Siddhartha Chib, Edward Greenberg
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Morphoscopic ancestry estimates in Filipino crania using multivariate probit regression models

American Journal of Physical Anthropology, 2020
AbstractObjectivesProbit has not been applied to ancestry estimation in forensic anthropology. The goals of this study were to: (1) evaluate the performance of probit analysis as a classification tool for ancestry estimation using ordinal data and (2) expand our current understanding of human cranial variation for an understudied population ...
Matthew C. Go, Joseph T. Hefner
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Bayesian inference in the multivariate probit model

2011
Correlated binary data arise in many applications. Any analysis of this type of data should take into account the correlation structure among the variables. The multivariate Probit model (MVP), introduced by Ashford and Snowden (1970), is a popular class of models particularly suitable for the analysis of correlated binary data. In this class of models,
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A probit model for multivariate random length ordinal data

Communications in Statistics - Theory and Methods, 1998
Multivariate random length ordinal data are data such that the ordinal response variable is observed a random number of times for each experimen¬tal unit. For example, depression may occur a random number of times and the severity of each depression episode is measured by an ordinal scale (e.g., l=mildly depressed, 2=moderately depressed, 3=very ...
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Bayesian analysis of multivariate ordered probit model with individual heterogeneity

AStA Advances in Statistical Analysis, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses

Computational Statistics & Data Analysis, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yonghai Li, Daniel W. Schafer
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Estimation of multivariate probit models by exact maximum likelihood [PDF]

, 2009
In this paper, we develop a new numerical method to estimate a multivariate probit model. To this end, we derive a new decomposition of normal multivariate integrals that has two appealing properties. First, the decomposition may be written as the sum of normal multivariate integrals, in which the highest dimension of the integrands is reduced relative
Jacques Huguenin, Florian Pelgrin, Alberto Holly   +2 more
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A latent variable probit model for multivariate ordered categorical responses

1999
This paper presents a fully Bayesian approach via Gibbs sampling for MIMIC models with ordered categorical outcomes. The method is of particular interest for moderate or medium sample size data situations as in the study to be presented. Compared to frequentist methods that are based on large sample theory, estimates and standard errors of parameters ...
Nikele, M., Fahrmeir, Ludwig
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Composite Likelihood Estimation for Multivariate Probit Latent Traits Models

Communications in Statistics - Theory and Methods, 2013
Inference in generalized linear mixed models with multivariate random effects is often made cumbersome by the high-dimensional intractable integrals involved in the marginal likelihood. This article presents an inferential methodology based on the marginal composite likelihood approach for the probit latent traits models.
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