Subset selection in quantile regression analysis via alternative Bayesian information criteria and heuristic optimization

Dünder E., Gumustekin S., Murat N., Cengiz M. A.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.46, no.22, pp.11091-11098, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 22
  • Publication Date: 2017
  • Doi Number: 10.1080/03610926.2016.1257718
  • Journal Name: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.11091-11098
  • Keywords: Quantile regression, Bayesian information, Subset selection, Optimization, Heuristic, VARIABLE SELECTION, MODEL SELECTION, TABU SEARCH
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Subset selection is an extensively studied problem in statistical learning. Especially it becomes popular for regression analysis. This problem has considerable attention for generalized linear models as well as other types of regression methods. Quantile regression is one of the most used types of regression method. In this article, we consider subset selection problem for quantile regression analysis with adopting some recent Bayesian information criteria. We also utilized heuristic optimization during selection process. Simulation and real data application results demonstrate the capability of the mentioned information criteria. According to results, these information criteria can determine the true models effectively in quantile regression models.