A Monte Carlo experiment is undertaken to examine the small sample properties of three alternative estimators of a bivariate probit model with selection. The three estimators are the censored probit estimator, single-equation probit applied to the selected sub-sample and single-equation probit applied to the full sample. These estimators are compared in terms of properties of coefficient estima...

Abstract This paper investigates the factors that drive farmers’ simultaneous adoption of six water conservation practices (WCPs) and intensity their adoption. We estimate these WCPs with a multivariate probit model, for adoption, an ordered model is estimated. Our results show gender, age, education, farm size (among other factors) influence probability extent WCPs. Furthermore, combinations l...

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. An inferentialmethodology based on themarginal pairwise likelihood approach is proposed. This method belonging to the broad class of composite likelihood involves marginal pairs probabilities of the responses wh...

We propose a Bayesian approach for inference in the multivariate probit model, taking into account the association structure between binary observations. We model the association through the correlation matrix of the latent Gaussian variables. Conditional independence is imposed by setting some off-diagonal elements of the inverse correlation matrix to zero and this sparsity structure is modele...

BACKGROUND Visual acuity measurement often results in an imprecise endpoint because subjects correctly identify some but not all of the letters on one or more size levels on a letter chart. The extent of this transition zone from seeing to nonseeing can be described by probit size, which is calculated by performing Probit Analysis on letter chart data. There has been no previous research into t...

SUMMARY. A rich class of parametric models is proposed for discrete choice data based on the scale mixtures of multivariate normal distributions. With special connections to multinomial probit, the new models can be implemented in a Bayesian framework without much diiculty. The proposed class of models can be extended to panel data where accounting for heterogeneities is needed. This is done by...

The development of accurate models and efficient algorithms for the analysis of multivariate categorical data are important and longstanding problems in machine learning and computational statistics. In this paper, we focus on modeling categorical data using Latent Gaussian Models (LGMs). We propose a novel logistic stick-breaking likelihood function for categorical LGMs that can exploit recent...

This 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. We discuss Bayesian estimation when a sample of independent clusters of varying sizes are available, and show how Gibbs s...

Abstract: This paper investigates the relationship between happiness (utility) and a host of socio-economic variables. The data set consists of a random sample of over 5,000 individuals from the Swedish adult population. Happiness is measured by a three-point categorical measure of overall happiness (not happy, happy sometimes, happy most of the time), and an ordered probit model is used to eco...