A stochastic restricted ridge regression estimator

Ozkale M. R.

JOURNAL OF MULTIVARIATE ANALYSIS, cilt.100, ss.1706-1716, 2009 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 100 Konu: 8
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.jmva.2009.02.005
  • Sayfa Sayıları: ss.1706-1716


Gross [J. Gross, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57-64] proposed a restricted ridge regression estimator when exact restrictions are assumed to hold. When there are stochastic linear restrictions on the parameter vector, we introduce a new estimator by combining ideas underlying the mixed and the ridge regression estimators under the assumption that the errors are not independent and identically distributed. Apart from [J. Gross, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57-64], we call this new estimator as the stochastic restricted ridge regression (SRRR) estimator. The performance of the SRRR estimator over the mixed estimator in respect of the variance and the mean square error matrices is examined. We also illustrate our findings with a numerical example. The shrinkage generalized least squares (GLS) and the stochastic restricted shrinkage GLS estimators are proposed. (C) 2009 Elsevier Inc. All rights reserved.