The Laplace likelihood method for estimating linear conditional quantile functions with right censored data proposed by Bottai and Zhang (2010) is demonstrated to be highly non-robust.

The literature is replete with variable selection techniques for the classical linear regression model. It is only relatively recently that authors have begun to explore variable selection in fully nonparametric and additive regression models. One such variable selection technique is a generalization of the LASSO called the group LASSO. In this work, we demonstrate a connection between the grou...

This article is devoted to study of the autoconvolution equations and generalized Mittag-Leffler functions. These types of equations are given in terms of the Laplace transform convolution of a function with itself. We state new classes of the autoconvolution equations of the first kind and show that the generalized Mittag-Leffler functions are solutions of these types of equations. In view of ...

A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the Laplace distribution is a scale mixture of a normal distribution. Finite sample performance of the proposed algorithm is evaluated by numerical simulation studies. The ...

Analyses of childhood stunting have mainly used mean regression yet modeling using quantile regression is more appropriate than using mean regression in that the former provides flexibility to analyze the determinants of stunting corresponding to quantiles of interest whereas the latter allows only analyzing the determinants of mean stunting. Bayesian structured additive quantile regression mod...