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Generalized difference-based weighted mixed almost unbiased ridge estimator in partially linear models

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Abstract

In this paper, a generalized difference-based estimator is introduced for the vector parameter \(\beta \) in partially linear model when the errors are correlated. A generalized difference-based almost unbiased ridge estimator is defined for the vector parameter \(\beta \). Under the linear stochastic constraint \(r=R\beta +e\), a new generalized difference-based weighted mixed almost unbiased ridge estimator is proposed. The performance of this estimator over the generalized difference-based weighted mixed estimator, the generalized difference-based estimator, and the generalized difference-based almost unbiased ridge estimator in terms of the mean square error matrix criterion is investigated. Then, a method to select the biasing parameter k and non-stochastic weight \(\omega \) is considered. The efficiency properties of the new estimator is illustrated by a simulation study. Finally, the performance of the new estimator is evaluated for a real dataset.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Çağ University, 33800, Tarsus-Mersin, Turkey

    Fikri Akdeniz

  2. Faculty of Mathematics, Statistics and Computer Science, Semnan University, P.O. Box 35195-363, Semnan, Iran

    Mahdi Roozbeh

Authors
  1. Fikri Akdeniz
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  2. Mahdi Roozbeh
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Correspondence to Fikri Akdeniz.

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Akdeniz, F., Roozbeh, M. Generalized difference-based weighted mixed almost unbiased ridge estimator in partially linear models. Stat Papers 60, 1717–1739 (2019). https://doi.org/10.1007/s00362-017-0893-9

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  • DOI: https://doi.org/10.1007/s00362-017-0893-9

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