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Generalized ridge estimation of a semiparametric regression model

  • Published:
Wuhan University Journal of Natural Sciences

Abstract

We considered the following semiparametric regression model T y i = X T i β + s(t i ) + e i (i = 1,2,...,n). First, the generalized ridge estimators of both parameters and non-parameters are given without a restrained design matrix. Second, the generalized ridge estimator will be compared with the penalized least squares estimator under a mean squares error, and some conditions in which the former excels the latter are given. Finally, the validity and feasibility of the method is illustrated by a simulation example.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002, Hubei, China

    Hongchang Hu & Shaolin Rao

Authors
  1. Hongchang Hu
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  2. Shaolin Rao
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Correspondence to Hongchang Hu.

Additional information

Foundation item: Supported by the Key Project of Chinese Ministry of Education (209078) and the Scientific Research Item of Hubei Provincial Department of Education (D20092207)

Biography: HU Hongchang, male, Professor, Ph. D., research directions: the applied probability and statistics.

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Hu, H., Rao, S. Generalized ridge estimation of a semiparametric regression model. Wuhan Univ. J. Nat. Sci. 15, 283–286 (2010). https://doi.org/10.1007/s11859-010-0652-4

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  • DOI: https://doi.org/10.1007/s11859-010-0652-4

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