Improved estimation in multiple linear regression models with measurement error and general constraint

  • Authors:

  • Hua Liang;Weixing Song

  • Affiliations:

  • Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA;Department of Statistics, Kansas State University, Manhattan, KS, 66506, USA

  • Venue:
  • Journal of Multivariate Analysis
  • Year:
  • 2009

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Abstract

In this paper, we define two restricted estimators for the regression parameters in a multiple linear regression model with measurement errors when prior information for the parameters is available. We then construct two sets of improved estimators which include the preliminary test estimator, the Stein-type estimator and the positive rule Stein type estimator for both slope and intercept, and examine their statistical properties such as the asymptotic distributional quadratic biases and the asymptotic distributional quadratic risks. We remove the distribution assumption on the error term, which was generally imposed in the literature, but provide a more general investigation of comparison of the quadratic risks for these estimators. Simulation studies illustrate the finite-sample performance of the proposed estimators, which are then used to analyze a dataset from the Nurses Health Study.