Regression diagnostics methods for Liu estimator under the general linear regression model


SÖKÜT AÇAR T., ÖZKALE ATICIOĞLU M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.49, no.3, pp.771-792, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 3
  • Publication Date: 2020
  • Doi Number: 10.1080/03610918.2019.1582781
  • 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.771-792
  • Keywords: Leverages, Influence measure, Liu estimator, Autocorrelation, Multicollinearity, INFLUENTIAL OBSERVATIONS, RIDGE-REGRESSION, BIASED-ESTIMATION
  • Çanakkale Onsekiz Mart University Affiliated: Yes

Abstract

This paper introduces the regression diagnostic methods for the Liu estimator under the general linear regression model with the autocorrelated error structure which is modeled by first-order autoregressive process. To identify the leverage observations, quasi-projection matrix is used, and to identify the influential observations DFFITS and Cook's D statistics which are single case deletion methods are used for the Liu estimator. For a special model with two explanatory variables, the leverage attitudes of the first observation are examined theoretically according to the autocorrelation coefficient and the Liu parameter. The proposed diagnostic methods are investigated through a numerical example.