Influence measures in ridge regression when the error terms follow an Ar(1) process

SÖKÜT AÇAR T., ÖZKALE ATICIOĞLU M. R.

COMPUTATIONAL STATISTICS, vol.31, no.3, pp.879-898, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 3
  • Publication Date: 2016
  • Doi Number: 10.1007/s00180-015-0615-5
  • Journal Name: COMPUTATIONAL STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.879-898
  • Keywords: Autocorrelation, Regression diagnostics, Ridge estimator, Multicollinearity, Generalized least squares estimator, CORRELATED ERRORS, LINEAR-REGRESSION, ESTIMATORS, MODEL
  • Çanakkale Onsekiz Mart University Affiliated: Yes

Abstract

Influence concepts have an important place in linear regression models and case deletion is a useful method for assessing the influence of single case. The influence measures in the presence of multicollinearity were discussed under the linear regression models when the errors structure is uncorrelated and homoscedastic. In contrast to other article on this subject, we consider the influence measures in ridge regression with autocorrelated errors. Theoretical results are illustrated with a numerical example and a Monte Carlo simulation is conducted to see the effect autocorrelation coefficient, strength of multicollinearity and sample size on leverage points and influential observations.