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Nonparametric Lack-of-fit Tests for Parametric Mean-Regression Model with Censored Data

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  • Olivier Lopez

    (Crest)

  • Valentin Patilea

    (Crest)

Abstract

We develop two kernel smoothing based tests of a parametric mean-regressionmodel against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by the synthetic data and the weightedleast squares approaches for estimating the parameters of a (non)linear regressionmodel under censoring. The asymptotic critical values of our tests are given by thequantiles of the standard normal law. The tests are consistent against ¯xed alter-natives, local Pitman alternatives and uniformly over alternatives in HÄolder classesof functions of known regularity.

Suggested Citation

  • Olivier Lopez & Valentin Patilea, 2007. "Nonparametric Lack-of-fit Tests for Parametric Mean-Regression Model with Censored Data," Working Papers 2007-01, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2007-01
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