Nonlinear Censored Regression Using Synthetic Data
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.
Year of publication: |
2006
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Authors: | Delecroix, Michel ; Lopez, Olivier ; Patilea, Valentin |
Institutions: | Centre de Recherche en Économie et Statistique (CREST), Groupe des Écoles Nationales d'Économie et Statistique (GENES) |
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