Cross validation of ridge regression estimator in autocorrelated linear regression models

Acar T. S., Ozkale M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.86, sa.12, ss.2429-2440, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 86 Sayı: 12
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1080/00949655.2015.1112392
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2429-2440
  • Anahtar Kelimeler: Autocorrelation, ridge regression, multicollinearity, ordinary cross validation, generalized cross validation, conceptual prediction, PERFORMANCE, PREDICTION, SIMULATION, ERRORS
  • Çukurova Üniversitesi Adresli: Evet

Özet

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.