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Likelihood estimation after nonparametric transformation

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

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.

Suggested Citation

  • Cheung, Ying-Kuen & Fine, Jason P., 2001. "Likelihood estimation after nonparametric transformation," Statistics & Probability Letters, Elsevier, vol. 55(1), pages 1-7, November.
    • Handle: RePEc:eee:stapro:v:55:y:2001:i:1:p:1-7
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    References listed on IDEAS

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    1. 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.
    2. 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 103-137, January.
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    Cited by:

    1. M. C. Jones, 2015. "On Families of Distributions with Shape Parameters," International Statistical Review, International Statistical Institute, vol. 83(2), pages 175-192, August.

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