Lifetime Data Analysis

, Volume 17, Issue 4, pp 594–607 | Cite as

Nonparametric quasi-likelihood for right censored data

Article

Abstract

Quasi-likelihood was extended to right censored data to handle heteroscedasticity in the frame of the accelerated failure time (AFT) model. However, the assumption of known variance function in the quasi-likelihood for right censored data is usually unrealistic. In this paper, we propose a nonparametric quasi-likelihood by replacing the specified variance function with a nonparametric variance function estimator. This nonparametric variance function estimator is obtained by smoothing a function of squared residuals via local polynomial regression. The rate of convergence of the nonparametric variance function estimator and the asymptotic limiting distributions of the regression coefficient estimators are derived. It is demonstrated in simulations that for finite samples the proposed nonparametric quasi-likelihood method performs well. The new method is illustrated with one real dataset.

Keywords

Kaplan–Meier estimate Local polynomial smoothing Semiparametric modeling Survival analysis Variance function 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Jiann-Ping Hsu college of Public HealthGeorgia Southern UniversityStatesboroUSA

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