Combining empirical likelihood and robust estimation methods for linear regression models


ÖZDEMİR Ş., ARSLAN O.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.51, no.3, pp.941-954, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1080/03610918.2019.1659968
  • Journal Name: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.941-954
  • Keywords: Empirical likelihood, Linear regression, M estimation, CONFIDENCE-INTERVALS
  • Ankara University Affiliated: Yes

Abstract

Ordinary least square (OLS) and robust methods are used for estimating the parameters of a linear regression model. These methods perform well under some distributional assumptions which may not be appropriate for some data sets. Therefore, nonparametric methods like Empirical likelihood (EL) may be considered. The EL method maximizes an EL function under some constraints. We consider the EL method with robustified constraints using M estimation method. We provide a small simulation study and a real data example to demonstrate the capability of robust EL method and results reveal that robust constraints are needed when outliers are resent in data.