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On the Multivariate Probit Model for Exchangeable Binary Data with Covariates
Biometrical Journal, 2005This paper considers the use of a multivariate binomial probit model for the analysis of correlated exchangeable binary data. The model can naturally accommodate both cluster and individual level covariates, while keeping a fairly flexible intracluster association structure.
Catalina, Stefanescu, Bruce W, Turnbull
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Testing for Dependence in Multivariate Probit Models
Biometrika, 1982SUMMARY A multivariate probit model is considered and the Lagrange multiplier or score statistic for testing independence is derived. The limiting distribution of the statistic takes a simple form under the null hypothesis and for local alternatives. The statistic is a natural generalization of Pearson's chi-squared for a 2 x 2 table.
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Identification in multivariate partial observability probit
International Journal of Mathematical Modelling and Numerical Optimisation, 2014Poirier (1980) considered a bivariate probit model in which the binary dependent variables y1 and y2 of a bivariate probit model were not observed individually, but the product z = y1 × y2 was observed. This paper expands this notion of partial observability to multivariate settings.
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Emergence of childhood psychiatric disorders: a multivariate probit analysis
Statistics in Medicine, 1998We applied a computationally practical form of probit analysis for multiple response variables to data on early childhood development of four psychiatric disorders: disruptive disorders (DD-attention deficit disorders, oppositional defiant disorder, conduct disorder); adjustment disorders (ADJ); emotional disorders (ED-all anxiety disorders, depression)
R D, Gibbons, J V, Lavigne
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Multivariate probit linear mixed models for multivariate longitudinal binary data
Statistics in MedicineWhen analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross‐correlations between different responses at different times, and correlations between ...
Kuo‐Jung Lee +3 more
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Multivariate probit models for conditional claim-types
Insurance: Mathematics and Economics, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Young, Gary +2 more
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A Bayesian multivariate probit for ordinal data with semiparametric random-effects
Computational Statistics & Data Analysis, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jung Seek Kim, Brian T. Ratchford
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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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Alternative Specifications of Multivariate Multilevel Probit Ordinal Response Models
Journal of Educational and Behavioral Statistics, 2003Multivariate multilevel models for ordinal variables are quite complex with respect to both interpretation and estimation. The specification in terms of a multivariate latent distribution and a set of thresholds helps in the interpretation of the variance-covariance parameters.
GRILLI, LEONARDO, RAMPICHINI, CARLA
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1998
Probit analysis is used in the environmental toxicology field as a procedure to study the dosage response relation in a population of biological organisms, where randomly chosen population members are exposed to various levels of applied stimulus and quantal response is assessed as either dead or alive.
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Probit analysis is used in the environmental toxicology field as a procedure to study the dosage response relation in a population of biological organisms, where randomly chosen population members are exposed to various levels of applied stimulus and quantal response is assessed as either dead or alive.
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