The Generalized Inverse Gaussian (GIG) distribution has found many interesting applications; see Jørgensen  [24]. This rich family includes some well-known distributions, such as the inverse Gaussian, gamma and exponential, as special cases. These distributions have been used as the mixing...

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...

In this paper, we introduce a new class of the slash distribution, an alpha skew normal slash distribution. The proposed model is more flexible in terms of its kurtosis than the slashed normal distribution and can efficiently capture the bimodality. Properties involving moments and moment...

This paper develops a stochastic model for individual claims reserving using observed data on claim payments as well as incurred losses. We extend the approach of Pigeon et al. (2013), designed for payments only, towards the inclusion of incurred losses. We call the new technique the individual...

For a skew-normal random sequence, convergence rates of the distribution of its partial maximum to the Gumbel extreme value distribution are derived. The asymptotic expansion of the distribution of the normalized maximum is given under an optimal choice of norming constants. We find that the...

Robust mixture modeling approaches using skewed distributions have recently been explored to accommodate asymmetric data. Parsimonious skew-t and skew-normal analogues of the GPCM family that employ an eigenvalue decomposition of a scale matrix are introduced. The methods are compared to...