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Nonlinear Censored Regression Using Synthetic Data

Author

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  • Michel Delecroix

    (Crest)

  • Olivier Lopez

    (Crest)

  • Valentin Patilea

    (Crest)

Abstract

The problem of estimating a nonlinear regression model when the dependent variableis randomly censored is considered. The parameter of the model is estimated by leastsquares using synthetic data, that is a suitable transformation of the response variablesthat preserves the conditional expectation. Two such transformations are considered.Consistency and asymptotic normality of the least squares estimators are derived. Theproofs are based on a novel approach that uses i.i.d. representation of synthetic datathrough Kaplan-Meier integrals. The asymptotic results are completed by a small com-parative simulation study.

Suggested Citation

  • Michel Delecroix & Olivier Lopez & Valentin Patilea, 2006. "Nonlinear Censored Regression Using Synthetic Data," Working Papers 2006-10, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2006-10
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    References listed on IDEAS

    as
    1. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    2. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
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