Performances of some high dimensional regression methods


KURNAZ F. S.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.50, no.6, pp.1820-1836, 2021 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 6
  • Publication Date: 2021
  • Doi Number: 10.1080/03610918.2021.1881115
  • Title of Journal : COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Page Numbers: pp.1820-1836
  • Keywords: lasso, L, (1) penalty, Robustness, Variable selection

Abstract

Variable selection is one of the important practical issues for many scientific areas, particularly in chemometrics, where data sets include several hundreds of variables and low number of observations. The aim of this paper is to compare some newly proposed variable selection methods by means of extensive simulation studies and to give some practical hints for use of the compared methods. Furthermore, we underpin the performances of compared methods based on real data examples.