Institute of Mathematical Statistics Lecture Notes - Monograph Series
Sieve estimates for biased survival data
In studies involving lifetimes, observed survival times are frequently censored and possibly subject to biased sampling. In this paper, we model survival times under biased sampling (a.k.a., biased survival data) by a semi-parametric model, in which the selection function $w(t)$ (that leads to the biased sampling) is specified up to an unknown finite dimensional parameter $\theta$, while the density function $f(t)$ of the survival times is assumed only to be smooth. Under this model, two estimators are derived to estimate the density function $f$, and a pseudo maximum likelihood estimation procedure is developed to estimate $\theta$. The identifiability of the estimation problem is discussed and the performance of the new estimators is illustrated via both simulation studies and a real data application.
First available in Project Euclid: 28 November 2007
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Copyright © 2006, Institute of Mathematical Statistics
Sun, Jiayang; Wang, Bin. Sieve estimates for biased survival data. Recent Developments in Nonparametric Inference and Probability, 127--143, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2006. doi:10.1214/074921706000000644. https://projecteuclid.org/euclid.lnms/1196284057