Cross validation of ridge regression estimator in autocorrelated linear regression models


Acar T. , ÖZKALE ATICIOĞLU M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol.86, no.12, pp.2429-2440, 2016 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 86 Issue: 12
  • Publication Date: 2016
  • Doi Number: 10.1080/00949655.2015.1112392
  • Title of Journal : JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Page Numbers: pp.2429-2440

Abstract

In this paper, we investigated the cross validation measures, namely OCV, GCV and Cp under the linear regression models when the error structure is autocorrelated and regressor data are correlated. The best performed ridge regression estimator is obtained by getting the optimal ridge parameter so as to minimize these measures. A Monte Carlo simulation study is given to see how the optimal ridge parameter is affected by autocorrelation and the strength of multicollinearity.