Combining the unrestricted estimators into a single estimator and a simulation study on the unrestricted estimators

Ozkale M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.82, sa.5, ss.653-688, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 82 Sayı: 5
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1080/00949655.2010.550293
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.653-688
  • Anahtar Kelimeler: multicollinearity, biased estimators, mean square error, PRINCIPAL COMPONENT REGRESSION, BIASED-ESTIMATION, RIDGE-REGRESSION, LIU ESTIMATOR
  • Çukurova Üniversitesi Adresli: Evet

Özet

The purpose of this paper is to combine several regression estimators (ordinary least squares (OLS), ridge, contraction, principal components regression (PCR), Liu, r - k and r - d class estimators) into a single estimator. The conditions for the superiority of this new estimator over the PCR, the r - k class, the r - d class, (beta) over cap (k, d), OLS, ridge, Liu and contraction estimators are derived by the scalar mean square error criterion and the estimators of the biasing parameters for this new estimator are examined. Also, a numerical example based on Hald data and a simulation study are used to illustrate the results.