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Nonlinear Censored Regression Using Synthetic Data
Author(s) -
DELECROIX MICHEL,
LOPEZ OLIVIER,
PATILEA VALENTIN
Publication year - 2008
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2007.00591.x
Subject(s) - mathematics , nonlinear regression , asymptotic distribution , estimator , consistency (knowledge bases) , statistics , strong consistency , nonlinear system , least squares function approximation , censored regression model , non linear least squares , regression , regression analysis , generalized least squares , discrete mathematics , physics , quantum mechanics
. The problem of estimating a nonlinear regression model, when the dependent variable is randomly censored, is considered. The parameter of the model is estimated by least squares using synthetic data. Consistency and asymptotic normality of the least squares estimators are derived. The proofs are based on a novel approach that uses i.i.d. representations of synthetic data through Kaplan–Meier integrals. The asymptotic results are supported by a small simulation study.