Olcay Arslan

TL;DR: The weighted least absolute deviation (WLAD) regression estimation method and the adaptive least absolute shrinkage and selection operator (LASSO) are combined to achieve robust parameter estimation and variable selection in regression simultaneously.

TL;DR: In this article, the authors proposed an alternative class of Liu-type M-estimators, which are obtained by shrinking an M-stimator beta M, instead of the OLS estimator using the matrix (X'X + I) -1 (X''X + dI).

TL;DR: In this paper, the scale mixture of the multivariate power exponential distribution introduced by Gomez et al. and the inverse generalized gamma distribution was introduced as a new family of multivariate distributions.

TL;DR: In this paper, the authors proposed to use new initial estimates for the calculation of the OSM-estimator, where the distribution of the error terms is Jones and Faddy's skewed t. The Monte-Carlo simulation study showed that the estimator(s) based on the proposed initial estimates is/are more efficient than the traditional initial estimates especially for the skewed cases.

TL;DR: In this article, a bimodal extension of the generalized gamma distribution is proposed by using a mixing approach, and the maximum likelihood estimators for the parameters of the new distribution are obtained.