Estimation in Linear Models with Censored Data
Copyright policy )Estimation in Linear Models with Censored Data
Summary: Three methods for linear regression with censored data are considered; that of \textit{J. Buckley} and \textit{I. James} [Biometrika 66, 429--436 (1979; Zbl 0425.62051)], a proposed simpler nonparametric method and a normal model for censored data. A new estimator for the variance of the error term in the Buckley and James procedure is proposed and simulations comparing the three methods are described.
- University of Michigan–Ann Arbor United States
- University of Michigan–Flint United States
- Louisiana State University United States
error variance estimation, Linear regression; mixed models, normal model, Kaplan-Meier estimator, nonparametric regression, censored data, simulations, Nonparametric estimation, EM algorithm
error variance estimation, Linear regression; mixed models, normal model, Kaplan-Meier estimator, nonparametric regression, censored data, simulations, Nonparametric estimation, EM algorithm
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