Alternative parameterizations and problems of identification and estimation of multivariate random effects models for categorical responses are investigated. The issues are illustrated in the context of the multivariate binomial logit-normal (BLN) model introduced by Coull and Agresti (2000, Biometrics 56, 73-80). We demonstrate that the BLN model is poorly identified unless proper restrictions...

In the present paper a mixed generalized estimating/pseudo-score equations (GEPSE) approach together with a distribution-free multiple imputation technique is proposed for the estimation of regression and correlation structure parameters of multivariate probit models with missing values for an ordered categorical time-invariant variable. Furthermore, a generalization of the squared trace correl...

probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. the problem of path overlapping in network modelling has been one of the main issues to be tackled. due to its flexible covariance structure, probit model can adequately address the pro...

Children and Women’s Participation Dynamics: Direct and Indirect Effects Children affect the after-birth labor force participation of women in two ways. Directly, the time spent in child-care reduces the labor market effort. Time spent out of the labor market while on maternity leave alters women’s participation experience and indirectly affects subsequent participation behavior. This paper pro...

Purpose – Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-t...

1 SUMMARY The development of adequate models for binary time series data with covariate adjustment has been an active research area in the last years. In the case, where interest is focused on marginal and association parameters, generalized estimating equations (GEE) (see for example Lipsitz, Laird and Harrington (1991) and Liang, Zeger and Qaqish (1992)) and likelihood (see for example Fitzma...

The main objective of organic agriculture is to produce healthy and quality food products without using synthetic chemical products in response to consumers’ concerns about food safety, human health, animal welfare and the environment. The EU’s promotion of organic agriculture constitutes an important alternative for society and for the more marginal agricultural regions in southern Europe. Fir...

The goal of this study is to introduce a statistical method regarding the analysis of specific latent data for regression analysis of the discrete data and to build a relation between a probit regression model (related to the discrete response) and normal linear regression model (related to the latent data of continuous response). This method provides precise inferences on binary and multinomia...

The current study contributes to the existing injury severity modeling literature by developing a multivariate probit model of injury severity and seat belt use decisions of both drivers involved in two-vehicle crashes. The modeling approach enables the joint modeling of the injury severity of multiple individuals involved in a crash, while also recognizing the endogeneity of seat belt use in p...

We first introduce a multivariate probit model for ordinal responses as implemented in R package mprobit. As before, we have n independent observations (Yi, X ′ i) with Yi = 0 or 1 as disease status andX ′ i = (Xi1, ..., Xik) as genotypes at k SNPs for subject i, i = 1, ..., n. In addition, we have some possible covariates A. We assume that there is a Latent Gaussian variable Z ′ i = (Zi1, ...,...