Premium
Smoothed empirical likelihood confidence intervals for the relative distribution with left‐truncated and right‐censored data
Author(s) -
Molaneslopez Elisa M.,
Cao Ricardo,
Keilegom Ingrid VAN
Publication year - 2010
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.10079
Subject(s) - censoring (clinical trials) , confidence distribution , statistics , nonparametric statistics , mathematics , pointwise , cdf based nonparametric confidence interval , confidence interval , empirical likelihood , truncation (statistics) , inference , statistical inference , robust confidence intervals , econometrics , computer science , mathematical analysis , artificial intelligence
The study of differences among groups is an interesting statistical topic in many applied fields. It is very common in this context to have data that are subject to mechanisms of loss of information, such as censoring and truncation. In the setting of a two‐sample problem with data subject to left truncation and right censoring, we develop an empirical likelihood method to do inference for the relative distribution. We obtain a nonparametric generalization of Wilks' theorem and construct nonparametric pointwise confidence intervals for the relative distribution. Finally, we analyse the coverage probability and length of these confidence intervals through a simulation study and illustrate their use with a real data set on gastric cancer. The Canadian Journal of Statistics 38: 453–473; 2010 © 2010 Statistical Society of Canada