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, 99 (4), 649-664

Empirical Likelihood Analysis of the Buckley-James Estimator

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Empirical Likelihood Analysis of the Buckley-James Estimator

Mai Zhou et al. J Multivar Anal.

Abstract

The censored linear regression model, also referred to as the accelerated failure time (AFT) model when the logarithm of the survival time is used as the response variable, is widely seen as an alternative to the popular Cox model when the assumption of proportional hazards is questionable. Buckley and James [Linear regression with censored data, Biometrika 66 (1979) 429-436] extended the least squares estimator to the semiparametric censored linear regression model in which the error distribution is completely unspecified. The Buckley-James estimator performs well in many simulation studies and examples. The direct interpretation of the AFT model is also more attractive than the Cox model, as Cox has pointed out, in practical situations. However, the application of the Buckley-James estimation was limited in practice mainly due to its illusive variance. In this paper, we use the empirical likelihood method to derive a new test and confidence interval based on the Buckley-James estimator of the regression coefficient. A standard chi-square distribution is used to calculate the P-value and the confidence interval. The proposed empirical likelihood method does not involve variance estimation. It also shows much better small sample performance than some existing methods in our simulation studies.

Figures

Fig. 1
Fig. 1
Q–Q plot of −2 log EL ratio, 5000 simulation run, sample size=100.
Fig. 2
Fig. 2
Q–Q plot of −2 log ELR (EL) and Wald statistics (BJvar). 1000 simulation runs.
Fig. 3
Fig. 3
Contour plot for the −2 log EL ratio, Stanford Heart Transplant Data, 152 cases.

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