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, ...,...

A comprehensive and comparative study of Broiler Turkey has been undertaken in Bangladesh focusing on the stability issues. This research aimed to compare financial profit Bangladesh's newly adopted those broilers. cross-sectional survey broiler turkey farmers areas provided information used study. 180 were chosen for using simple random purposive sampling techniques. The data collected analyze...

We present a scalable Bayesian model for lowrank factorization of massive tensors with binary observations. The proposed model has the following key properties: (1) in contrast to the models based on the logistic or probit likelihood, using a zero-truncated Poisson likelihood for binary data allows our model to scale up in the number of ones in the tensor, which is especially appealing for mass...

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...

Binary response regression is a useful technique for analyzing categorical data. Popular binary models use special link functions such as the logit or the probit link. In this article, the inverse link function H is modeled to be a scale mixture of cumulative distribution functions. Two different models for H are proposed: (i) H is a finite normal scale mixture with a Dirichlet distribution pri...

A very general class of multilevel factor analysis and structural equation models is proposed which are derived from considering the concatenation of a series of building blocks that use sets of factor structures defined within the levels of a multilevel model. An MCMC estimation algorithm is proposed for this structure to produce parameter chains for point and interval estimates. We show how t...

This article provides a framework for estimating the marginal likelihood for the purpose of Bayesian model comparisons . The approach extends and completes the method presented in Chib (1995) by overcoming the problems associated with the presence of intractable full conditional densities. The proposed method is developed in the context of MCMC chains produced by the Metropolis–Hastings algorit...

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sam...

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...

Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. We use a...