BACKGROUND Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. METHODS In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected...

Corporate directors are influenced by overconfidence, which is one of the personality traits of individuals; it may take irrational decisions that will have a significant impact on the company's performance in the long run. The purpose of this paper is to validate and compare the Naive Bayesian Classification algorithm and probit regression in the prediction of Management's overconfident at pre...

Probit regression and logistic regression are well-known models for classification. In contrast to logistic regression, probit regression has a canonical generalization that allows us to model correlations between the labels. This is a way to include metadata into the model that correlate the noisy observation process. We show that the approach leads to the mathematical problem of integrating a...

Abstract: We present a class of sparse generalized linear models that include probit and logistic regression as special cases and offer some extra flexibility. We provide an EM algorithm for learning the parameters of these models from data. We apply our method in text classification and in simulated data and show that our method outperforms the logistic and probit models and also the elastic n...

We consider a discrete choice model in which the payoffs to each of an agent’s n actions are subjected to the average of m i.i.d. shocks, and use tools from large deviations theory to characterize the rate of decay of the probability of choosing a given suboptimal action as m approaches infinity. Our model includes the multinomial probit model of Myatt and Wallace (2003) as a special case. We s...

We consider the problem of gene selection and classification based on the expression data. Specifically, we propose a bootstrap Bayesian gene selection method for nonlinear probit regression. A binomial probit regression model with data augmentation is used to transform the binomial problem into a sequence of smoothing problems. The probit regressor is approximated as a nonlinear combination of...

We introduce a generalized skew probit (gsp) class of links for the modeling of binary regression giving some properties and conditions for the existence of the maximum likelihood estimator and of the posterior distributions of the parameters of the model when improper uniform priors are established. As shown, asymmetric links already proposed in the literature are special cases of the general ...