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Low dimensional semiparametric estimation in a censored regression model

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  • Moral-Arce, Ignacio
  • Rodríguez-Póo, Juan M.
  • Sperlich, Stefan

Abstract

A new estimation procedure for a partial linear additive model with censored responses is proposed. To this aim, ideas of Lewbel and Linton [A. Lewbel, O. Linton, Nonparametric censored and truncated regression, Econometrica 70 (2002) 765-779] on censored model regression are combined with those of Kim et al. [W. Kim, O. Linton, N.W. Hengartner, A computationally efficient estimator for additive nonparametric regression with bootstrap confidence intervals, Journal of Computational and Graphical Statistics, 8 (1999) 278-297] on marginal integration and those on average derivatives. This allows for dimension reduction, interpretability and -- depending on the context -- for weights yielding computationally attractive estimates. Asymptotic behavior is provided for all proposed estimators.

Suggested Citation

  • Moral-Arce, Ignacio & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 2011. "Low dimensional semiparametric estimation in a censored regression model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 118-129, January.
  • Handle: RePEc:eee:jmvana:v:102:y:2011:i:1:p:118-129
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    References listed on IDEAS

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    Cited by:

    1. Maria Karlsson & Thomas Laitila, 2014. "Finite mixture modeling of censored regression models," Statistical Papers, Springer, vol. 55(3), pages 627-642, August.

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