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Jackknifing type weighted least squares estimators in partially linear regression models

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  • You, Jinhong
  • Sun, Xiaoqian
  • Pang, Wan-kai
  • Leung, Ping-kei

Abstract

In a heteroskedastic partially linear regression model, You and Chen (Technical Report, Department of Mathematics and Statistics, University of Regina, 2000) proposed a semiparametric generalized least squares estimator (SGLSE). In this paper, a jackknife-type estimator of the asymptotic covariance matrix of the SGLSE is proposed. It is shown that this jackknife-type estimator is consistent and performs better than the usual [delta] method in some cases.

Suggested Citation

  • You, Jinhong & Sun, Xiaoqian & Pang, Wan-kai & Leung, Ping-kei, 2002. "Jackknifing type weighted least squares estimators in partially linear regression models," Statistics & Probability Letters, Elsevier, vol. 60(1), pages 17-31, November.
    • Handle: RePEc:eee:stapro:v:60:y:2002:i:1:p:17-31
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    References listed on IDEAS

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    7. Schick, Anton, 1994. "Estimation of the autocorrelation coefficient in the presence of a regression trend," Statistics & Probability Letters, Elsevier, vol. 21(5), pages 371-380, December.
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