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Biometrika 1998 85(4):980-986; doi:10.1093/biomet/85.4.980
© 1998 by Biometrika Trust
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MISCELLANEA

On the linear transformation model for censored data

J. P. FINE, Z. YING and L. G. WEI

Department of Biostatistics, Harvard University Boston, Massachusetts 02115, U.S.A.jpf{at}hsph.harvard.edu
Department of Statistics, Rutgers University Piscataway, New Jersey 08855, U.S.A.zying{at}stat.rutgers.edu
Department of Biostatistics, Harvard University Boston, Massachusetts 02115, U.S.A.wei{at}hsph.harvard.edu

Recently Cheng, Wei & Ying (1995, 1997) proposed a class of estimation procedures for semiparametric linear transformation models with censored observations. When the support of the censoring variable is shorter than that of the failure time, the estimators are asymptotically biased. In this paper, we present a simple modification of Cheng's estimation procedures for the regression parameters. Through extensive numerical studies with practical sample sizes, we find that the new proposals perform well, but the original interval estimators may not have correct coverage probabilities when censoring is heavy. Prediction procedures for the survival probabilities of future subjects are also modified accordingly.

Key Words: Gaussian process • Proportional hazards model • Proportional odds model • Weighted least squares


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