A critical issue in modelling binary response data is the choice of the links. We introduce a new link based on the Student t-distribution (t-link) for correlated binary data. The t-link relates to the common probit-normal link adding one additional parameter which purely controls the heaviness of the tails of the link. We propose an interesting EM algorithm for computing the maximum likelihood...

In a binary response regression model, classical residuals are diicult to deene and interpret due to the discrete nature of the response variable. In contrast, Bayesian residuals have continuous-valued posterior distributions which can be graphed to learn about outlying observations. Two deenitions of Bayesian residuals are proposed for binary regression data. Plots of the posterior distributio...

Interdependence—i.e., that the outcomes in or actions or choices of some units depend on those in/of others—is substantively and theoretically ubiquitous in and central to binary outcomes of interest across the social sciences. Most empirical applications omit interdependence, however; even theoretical and substantive discussion usually ignores it. Moreover, in the few contexts where spatial in...

In a binary response regression model, classical residuals are diicult to deene and interpret due to the discrete nature of the response variable. In contrast , Bayesian residuals have continuous-valued posterior distributions which can be graphed to learn about outlying observations. Two deenitions of Bayesian residuals are proposed for binary regression data. Plots of the posterior distributi...

This paper provides a practical simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods and maximum likelihood estimates are obtained by a Monte Carlo version of the EM algorithm. A practical approach for the computation of Bayes factors from the simulation outp...

Random intercept models for binary data are useful tools for addressing betweensubject heterogeneity. Unlike linear models, the non-linearity of link functions used for binary data force a distinction between marginal and conditional interpretations. This distinction is blurred in probit models with a normally distributed random intercept because the resulting model implies a probit marginal li...

This paper avails of the multinational-level Amadeus dataset to study the dynamics of the European pharmaceutical industry in the Single Market Programme era: 1990-2004. Our unit of investigations are multinationals that every period decide whether to expand or not, by capturing at least one of the arisen opportunities. By using a dynamic panel probit approach with unobserved heterogeneity we s...

The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multidimensional constrained space of latent variables, significantly limits its application in practice. We propose a flexible deep generalization of the clas...

In this paper estimators for distribution free heteroskedastic binary response models are proposed. The estimation procedures are based on relationships between distribution free models with a conditional median restriction and parametric models (such as Probit/Logit) exhibiting (multiplicative) heteroskedasticity. The first proposed estimator is based on the observational equivalence between t...