We examine the problem of jointly selecting the number of components and variables in finite mixture regression models. We find that the Akaike information criterion is unsatisfactory for this purpose because it overestimates the number of components, which in turn results in incorrect variables being retained in the model. Therefore, we derive a new information criterion, the mixture regressio...

Regression models based on the log-symmetric family of distributions are particularly useful when response variable is continuous, positive, and asymmetrically distributed. In this article, we propose derive a class new approach to quantile regression using parameterized by means their quantiles. Two Monte Carlo simulation studies conducted utilizing R software. The first one analyzes performan...

This report describes a line linker based on tools of Frequentist Statistics: Chi-square signiicance testing and the Akaike Information Criterion. The line linker has been implemented as an AVS T M module and is available as freeware. This report describes the basic facts about the distributed linker and its use.

A multivariate distribution can be represented in terms of its underlying margins by binding these margins together using a copula function (Sklar, 1959). Here, we propose a new class of survival FGM type modification truncated copulas which quantify dependency and incorporate directional dependence. In addition, we apply our proposed methods to the analysis of directional dependence relationsh...

Bayesians often reject the Akaike Information Criterion (AIC) because it introduces ideas that do not fit into their philosophy of statistical inference. Here we show that a difference in the AIC scores that two models receive is evidence that they differ in their degrees of predictive accuracy, where evidence is understood in terms of the Law of Likelihood. Since the Law of Likelihood is a cen...

In the standard stochastic frontier model with sample selection, the two components of the error term are assumed to be independent, and the joint distribution of the unobservable in the selection equation and the symmetric error term in the stochastic frontier equation is assumed to be bivariate normal. In this paper, we relax these assumptions by using two copula functions to model the depend...