Weighted Bootstrap and Edgeworth Expansion for the Nonparametric Estimator of Time-Dependent AUC
Date Issued
2007
Date
2007
Author(s)
Wang, Shao-Hsuan
DOI
en-US
Abstract
The confidence region for the time-dependent area under the receiver operating
characteristic curve (AUC) has been constructed based on the asymptotic
normality of a non-parametric estimator. However, the performance
of the normal approximated confidence interval is very poor when the sample
size is small and the censoring rate is high. To improve the coverage
probability and the accuracy of confidence interval, the random weighted
bootstrap distribution and the Edgeworth expansion with remainder term
o(n^(?1/2)) are proposed to approximate the sampling distribution of the estimator.
The asymptotic properties of the random weighted bootstrap approximation
and the Edgeworth expansion are studied in this thesis. The
usefulness of the proposed procedures are confirmed by a class of simulations
with different sample sizes and censoring rates. Moreover, the methods are
demonstrated using the ACTG 175 data.
Subjects
AUC
Edgeworth展式
kaplan-meier估計式
常態逼近理論
存活資料
加權自助法
U統計量
Edgeworth expansion
Kaplan-Meier estimator
normal approximation
random weighted
Type
thesis
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