Skew Gaussian Process for Nonlinear Regression

M. T. Alodat and E. Y. Al-Momani

Communications in Statistics - Theory and Methods, 2014, vol. 43, issue 23, 4936-4961

Abstract: In this article, we extend the Gaussian process for regression model by assuming a skew Gaussian process prior on the input function and a skew Gaussian white noise on the error term. Under these assumptions, the predictive density of the output function at a new fixed input is obtained in a closed form. Also, we study the Gaussian process predictor when the errors depart from the Gaussianity to the skew Gaussian white noise. The bias is derived in a closed form and is studied for some special cases. We conduct a simulation study to compare the empirical distribution function of the Gaussian process predictor under Gaussian white noise and skew Gaussian white noise.

Date: 2014
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DOI: 10.1080/03610926.2012.737498

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