Predicting the functional roles of proteins based on various genome-wide data, such as protein-protein association networks, has become a canonical problem in computational biology. Approaching this task as a binary classification problem, we develop a network-based extension of the spatial auto-probit model. In particular, we develop a hierarchical Bayesian probit-based framework for modeling ...

Logistic and probit regression models are commonly used in practice to analyze binary response data, but the maximum likelihood estimators of these models are not robust to outliers. This paper considers a robit regression model, which replaces the normal distribution in the probit regression model with a t-distribution with a known or unknown number of degrees of freedom. It is shown that (i) ...

Recently, the null category noise model has been proposed as a simple and elegant solution to the problem of incorporating unlabeled data into a Gaussian process (GP) classification model. In this paper, we show how this binary likelihood model can be generalised to the multi-class setting through the use of the multinomial probit GP classifier. We present a Gibbs sampling scheme for sampling t...

In environmental management, we often have to deal with binary response variables whose outcome dictates the course of action. This paper introduces a nonparametric Bayesian binary regression model that is more exible than the commonly used logistic or probit models. Due to the Bayesian feature, the model can be easily used to combine observed data with our knowledge of the subject to produce s...

We address function misspecification and model heterogeneity, two critical issues in empirical work. A nonparametric approach is proposed for single-index, binary-choice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without knowing the conditional pro...

We propose a state space mixed models for binary time series where the inverse link function is modeled to be a cumulative distribution function of the scale mixture of normal (SMN) distributions. Specific inverse links examined include the normal, Student-t, slash and the variance gamma links. We use the threshold latent approach (Albert and Chib, 1993) to represent the binary system as a line...

Agricultural researchers, especially research entomologists, are interested in determining how mortality rates change with increasing dose levels of a certain stimulus (e.g., pesticide, drug). A researcher may have a particular interest in determining the pesticide dose at which 50% (LD50), 75% (LD75), or 90% (LD90) of insect populations respond. Several procedures have been suggested for estim...

DCM (Discrete Choice Models) is a package, written in Ox, for estimating a class of discrete choice models. DCM represents an important development for both the OxMetric and, more generally, microeconometric computing environment in making available a broad range of discrete choice models, including standard binary response models, with notable extensions including conditional mixed logit, mixe...

Many researchers believe that consumers’ decisions are not only decided by their personal tastes, but also by the decisions of people who are in their networks. On the other hand, social scientists are more interested in consumers’ dichotomous choice. So an auto-probit model accommodating multiple networks are very useful. However, Current methods to investigate multiple autocorrelated network ...