We propose a novel Bayesian model framework for discrete ordinal and count data based on conditional transformations of the responses. The transformation function is estimated from in conjunction with an priori chosen reference distribution. For responses, resulting sense that it fully parametric yet distribution-free approach can additionally account excess zeros additive specifications. categ...

Misclassification of a binary response variable and nonrandom sample selection are data issues frequently encountered by empirical researchers. For cases in which both feature simultaneously set, we formulate model for misclassified outcome the conditional probabilities misclassification allowed to depend on covariates. Assuming availability validation data, pseudo-maximum likelihood technique ...

We are addressing two fundamental problems in authorship verification (AV): Topic variability and miscalibration. Variations the topic of disputed texts a major cause error for most AV systems. In addition, it is observed that underlying probability estimates produced by deep learning mechanisms oftentimes do not match actual case counts respective training data. As such, poorly calibrated. exp...

Consider a clinical trial in which the main end-point is survival. Suppose after the start of the study an intermediate event occurs which may be influenced by a covariate(or treatment). In many clinical studies the occurrence of an intermediate event may change the survival distribution. This investigation develops two-stage model which, in the first stage, models the effect of covariate on th...