is equivalent to probit, logit, and related models. However, the formulation that assumes latent variable Zi is allowing Gibbs sampling scheme (eg., Chib and Albert 1993) and Johnson and Albert (1999)). Successive sampling from full conditionals, (i) [β|Z, Y ] and (ii) [Z|β, Y ]. Assume that F is normal distribution and that the above model is probit. Then the distribution for β given Z is simp...

We consider latent structural versions of probit loss and ramp loss. We show that these surrogate loss functions are consistent in the strong sense that for any feature map (finite or infinite dimensional) they yield predictors approaching the infimum task loss achievable by any linear predictor over the given features. We also give finite sample generalization bounds (convergence rates) for th...

Probit models belong to the class of latent variable threshold models for analyzing binary data. They arise by assuming that the binary response is the indicator of the event that an unobserved latent variable exceeds a given threshold. Estimation can be done either in a likelihood or a Bayesian framework. The probit models can be generalized for the analysis of a variety of qualitative and lim...

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...

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 selec...

We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological c...

The main reason for carrying out this study was to determine possible relationships among several adoption parameters of computer use, internet usage and internet access in agriculture. The key options for determining relationships (apart from non-parametric correlation techniques) are canonical correlation analysis, probit models and logit models. Canonical correlation analysis is generally se...

The paper provides probability estimates of the state of the GDP growth. A regime-switching model defines the probability of the Greek GDP being in boom or recession. Then probit models extract the predictive information of a set of explanatory (economic and financial) variables regarding the state of the GDP growth. A contemporaneous, as well as a lagged, relationship between the explanatory v...

van Soestbergen, A. A. (Brookhaven National Laboratory, Upton, N.Y.). X irradiation of a sensitive and a resistant strain of Escherichia coli under controlled oxygen concentrations. J. Bacteriol. 89:1032-1034. 1965.-Radiosensitive and radioresistant strains of Escherichia coli were given an X-ray dose of 4,544 rad at various controlled O(2) concentrations, and the results were subjected to prob...

Workplace drug testing programs are often met with intense criticism. Despite resistance among labor and consumer groups and a lack of rigorous empirical evidence regarding effectiveness, drug testing programs have remained popular with employers throughout the 1990s and into the current century. The present study analyzed nationally representative data on over 15,000 US households to determine...