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Efficiency of the generalized-difference-based weighted mixed almost unbiased two-parameter estimator in partially linear model

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  • Fikri Akdeniz
  • Mahdi Roozbeh

Abstract

In this paper, a generalized difference-based estimator is introduced for the vector parameter β in partially linear model when the errors are correlated. A generalized-difference-based almost unbiased two-parameter estimator is defined for the vector parameter β. Under the linear stochastic constraint r = Rβ + e, we introduce a new generalized-difference-based weighted mixed almost unbiased two-parameter estimator. The performance of this new estimator over the generalized-difference-based estimator and generalized- difference-based almost unbiased two-parameter estimator in terms of the MSEM criterion is investigated. 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.

Suggested Citation

  • Fikri Akdeniz & Mahdi Roozbeh, 2017. "Efficiency of the generalized-difference-based weighted mixed almost unbiased two-parameter estimator in partially linear model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(24), pages 12259-12280, December.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:24:p:12259-12280
    DOI: 10.1080/03610926.2017.1295075
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    Cited by:

    1. M. Taavoni & M. Arashi, 2021. "Kernel estimation in semiparametric mixed effect longitudinal modeling," Statistical Papers, Springer, vol. 62(3), pages 1095-1116, June.

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