This paper develops an alternative method for gene selection that combines model based clustering and binary classification. By averaging the covariates within the clusters obtained from model based clustering, we define “meta-covariates” and use them to build a probit regression model, thereby selecting clusters of similarly behaving genes, aiding interpretation. This simultaneous learning tas...

It is known that the robit regression model for binary data is a robust alternative to the more popular probit and logistic models. The robit model is obtained by replacing the normal distribution in the probit regression model with the Student’s t distribution. Unlike the probit and logistic models, the robit link has an extra degrees of freedom (df) parameter. It is shown that in practice it ...

The nonlinear fixed-effects model has two shortcomings, one practical and one methodological. The practical obstacle relates to the difficulty of computing the MLE of the coefficients of non-linear models with possibly thousands of dummy variable coefficients. In fact, in many models of interest to practitioners, computing the MLE of the parameters of fixed effects model is feasible even in pan...

This paper presents a new user-written STATA command called ivtreatreg for the estimation of five different (binary) treatment models with and without idiosyncratic (or heterogeneous) average treatment effect. Depending on the model specified by the user, ivtreatreg provides consistent estimation of average treatment effects both under the hypothesis of “selection on observables” and “selection...

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...

The spatial model has been in use in political science for close to 30 years, and in that period it has achieved a place of prime importance as our paradigm of the process of candidatechoice used by voters. For much of this time political scientists have estimated models of candidatechoice using binary logit or probit, even in cases where there were more than two choices facing voters. Recently...

BACKGROUND An intermediate endpoint is hypothesized to be in the middle of the causal sequence relating an independent variable to a dependent variable. The intermediate variable is also called a surrogate or mediating variable and the corresponding effect is called the mediated, surrogate endpoint, or intermediate endpoint effect. Clinical studies are often designed to change an intermediate o...

The nonlinear fixed effects model has two shortcomings, one practical, one methodological. The practical obstacle relates to the difficulty of estimating nonlinear models with possibly thousands of dummy variable coefficients. In fact, in many models of interest to practitioners, estimation of the fixed effects model is feasible even in panels with very large numbers of groups. The result, thou...