We propose a three-parameter skew-normal distribution, obtained by using hidden truncation on a skew-normal random variable. The hidden truncation framework permits direct interpretation of the model parameters. A link is established between the model and the closed skew-normal distribution.

A generalization of the Probit model is presented, with the extended skew-normal cumulative distribution as a link function, which can be used for modelling a binary response variable in the presence of selectivity bias. The estimate of the parameters via ML is addressed, and inference on the...

In this article, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression (ESGP) model. The ESGP model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGP model at...