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Likelihood estimation after nonparametric transformation

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  • Cheung, Ying-Kuen
  • Fine, Jason P.
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    Abstract

    We propose a two-step likelihood estimation procedure for the coefficients in a semiparametric transformation model. A simple nonparametric estimator for the unknown transformation is substituted into the likelihood. The resulting maximiser is shown to be consistent and asymptotically normal. Numerical studies indicate that the estimator may be as precise as an efficient semiparametric procedure.

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    File URL: http://www.sciencedirect.com/science/article/B6V1D-443525G-1/2/ca39f2567878284af43d07806ca81d24
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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 55 (2001)
    Issue (Month): 1 (November)
    Pages: 1-7

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    Handle: RePEc:eee:stapro:v:55:y:2001:i:1:p:1-7

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    Related research

    Keywords: Transformation model Pseudo-likelihood Semiparametric efficiency;

    References

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    1. Horowitz, Joel L, 1996. "Semiparametric Estimation of a Regression Model with an Unknown Transformation of the Dependent Variable," Econometrica, Econometric Society, vol. 64(1), pages 103-37, January.
    2. Gorgens, Tue & Horowitz, Joel L., 1999. "Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable," Journal of Econometrics, Elsevier, vol. 90(2), pages 155-191, June.
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