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Asymptotic efficiency of ridge estimator in linear and semiparametric linear models

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  • Luo, June

Abstract

The linear model with a growing number of predictors arises in many contemporary scientific endeavor. In this article, we consider the commonly used ridge estimator in linear models. We propose analyzing the ridge estimator for a finite sample size n and a growing dimension p. The existence and asymptotic normality of the ridge estimator are established under some regularity conditions when p→∞. It also occurs that a strictly linear model is inadequate when some of the relations are believed to be of certain linear form while others are not easily parameterized, and thus a semiparametric partial linear model is considered. For these semiparametric partial linear models with p>n, we develop a procedure to estimate the linear coefficients as if the nonparametric part is not present. The asymptotic efficiency of the proposed estimator for the linear component is studied for p→∞. It is shown that the proposed estimator of the linear component asymptotically performs very well.

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  • Luo, June, 2012. "Asymptotic efficiency of ridge estimator in linear and semiparametric linear models," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 58-62.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:1:p:58-62
    DOI: 10.1016/j.spl.2011.08.019
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    1. Yatchew, A., 1997. "An elementary estimator of the partial linear model," Economics Letters, Elsevier, vol. 57(2), pages 135-143, December.
    2. Luo, June, 2010. "The discovery of mean square error consistency of a ridge estimator," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 343-347, March.
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    Cited by:

    1. Luo, June & Gerard, Patrick, 2013. "Using thresholding difference-based estimators for variable selection in partial linear models," Statistics & Probability Letters, Elsevier, vol. 83(12), pages 2601-2606.
    2. Luo, June & Kulasekera, K.B., 2013. "Error covariance matrix estimation using ridge estimator," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 257-264.

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