Nonlinear Censored Regression Using Synthetic Data

Michel Delecroix, Olivier Lopez and Patilea Patilea
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Michel Delecroix: Crest
Olivier Lopez: Crest
Patilea Patilea: Crest

No 2006-10, Working Papers from Center for Research in Economics and Statistics

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.

Pages: 23
Date: 2006
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