Journal
SCANDINAVIAN JOURNAL OF STATISTICS
Volume 35, Issue 2, Pages 248-265Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1467-9469.2007.00591.x
Keywords
asymptotic normality; consistency; Kaplan-Meier integral; nonlinear regression; right censoring; synthetic data
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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.
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