Recently, the ensemble learning approaches have been proven to be quite effective for variable selection in linear regression models. In general, a good variable selection ensemble should consist of a diverse collection of strong members. Based on the parallel genetic algorithm (PGA) proposed in...

Time-varying coefficient models with autoregressive and moving-average-generalized autoregressive conditional heteroscedasticity structure are proposed for examining the time-varying effects of risk factors in longitudinal studies. Compared with existing models in the literature, the proposed...

This paper examines the existence of time trends in the infant mortality rates in a number of countries in the twentieth century. We test for the presence of deterministic trends by adopting a linear model for the log-transformed data. Instead of assuming that the error term is a stationary...

Empirical Bayes estimator for the transition probability matrix is worked out in the cases where we have belief regarding the parameters, For example, where the states seem to be equal or not. In both cases, priors are in accordance with our beliefs. Using EM algorithm, computational methods for...

Modeling the relationship between multiple financial markets has had a great deal of attention in both literature and real-life applications. One state-of-the-art technique is that the individual financial market is modeled by generalized autoregressive conditional heteroskedasticity (GARCH)...

The problem of heavy tail in regression models is studied. It is proposed that regression models are estimated by a standard procedure and a statistical check for heavy tail using residuals is conducted as a tool for regression diagnostic. Using the peaks-over-threshold approach, the generalized...

The literature devoted to the export-led growth (ELG) hypothesis, which is of utmost importance for policymaking in emerging countries, provides mixed evidence for the validity of the hypothesis. Recent contributions focus on the time-dependence of the relationship between export and output...

The aim of this paper is to define a new approach for the analysis of data collected by means of SERVQUAL questionnaires which is based on the generalized cross entropy (GCE) approach. In this respect, we firstly give a short review about the important role that SERVQUAL plays in the analysis of...

We study the problem of fitting a heteroscedastic median regression model with doubly truncated data. A self-consistency equation is proposed to obtain an estimator. We set up a least absolute deviation estimating function. We establish the consistency and asymptotic normality for the case when...

Lasso methods are regularisation and shrinkage methods widely used for subset selection and estimation in regression problems. From a Bayesian perspective, the Lasso-type estimate can be viewed as a Bayesian posterior mode when specifying independent Laplace prior distributions for the...