Shrinkage ridge estimators in semiparametric regression models

Mahdi Roozbeh

Journal of Multivariate Analysis, 2015, vol. 136, issue C, 56-74

Abstract: In this paper, ridge and non-ridge type shrinkage estimators and their positive parts are defined in the semiparametric regression model when the errors are dependent and some non-stochastic linear restrictions are imposed under a multicollinearity setting. The exact risk expressions in addition to biases are derived for the estimators under study and the region of optimality of each estimator is exactly determined. Also, necessary and sufficient conditions, for the superiority of the ridge type estimator over its counterpart, for selecting the ridge parameter k are obtained. Lastly, a simulation study and real data analysis are performed to illustrate the efficiency of proposed estimators based on the minimum risk criterion. In this regard, kernel smoothing and modified cross-validation methods for estimating the non-parametric function are used.

Keywords: Generalized restricted ridge estimator; Kernel smoothing; Linear restriction; Multicollinearity; Positive-rule shrinkage; Semiparametric regression model; Stein-type shrinkage (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.jmva.2015.01.002

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