The Multinomial Logit, discrete choice model of transport demand, has several restrictions when compared with the more general Multinomial Probit model. The most famous of these are that unobservable components of utilities should be mutually independent and homoskedastic. Correlation can be accommodated to a certain extent by the Hierarchical Logit model, but the problem of heteroskedasticity ...

The purpose of this study is to compare the performances of Artificial Neural Networks (ANN) and Multinomial Probit (MNP) approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 h...

This project deals with the estimation of Logistic Regression parameters. We first review the binary logistic regression model and the multinomial extension, including standard MAP parameter estimation with a Gaussian prior. We then turn to the case of Bayesian Logistic Regression under this same prior. We review the cannonical approach of performing Bayesian Probit Regression through auxiliary...

Consider a probit regression problem in which Y1, . . . ,Yn are independent Bernoulli random variables such that Pr.Yi D1/DΦ.xT i β/ where xi is a p-dimensional vector of known covariates that are associated with Yi ,β is a p-dimensional vector of unknown regression coefficients and Φ. / denotes the standard normal distribution function. We study Markov chain Monte Carlo algorithms for explorin...

Consider a probit regression problem in which Y1, . . . , Yn are independent Bernoulli random variables such that Pr(Yi = 1) = Φ(xi β) where xi is a p-dimensional vector of known covariates associated with Yi, β is a p-dimensional vector of unknown regression coefficients and Φ(·) denotes the standard normal distribution function. We study Markov chain Monte Carlo algorithms for exploring the i...

We consider the problems of multi-class cancer classification from gene expression data. After discussing the multinomial probit regression model with Bayesian gene selection, we propose two Bayesian gene selection schemes: one employs different strongest genes for different probit regressions; the other employs the same strongest genes for all regressions. Some fast implementation issues for B...

INTRODUCTION This study aimed to assess the susceptibility of Culex quinquefasciatus larvae to two pyrethroids (Cypermethrin and Deltamethrin), two derivatives of Avermectin (Ivermectin and Abamectin) and an organophosphate (Temephos). METHODS Third- and fourth-instar larvae of C. quinquefasciatus were exposed to different concentrations of insecticides (eleven repetitions) according to the W...

Interval-censored data occur naturally in many fields and the main feature is that the failure time of interest is not observed exactly, but is known to fall within some interval. In this paper, we propose a semiparametric probit model for analyzing case 2 interval-censored data as an alternative to the existing semiparametric models in the literature. Specifically, we propose to approximate th...