Measure of Dependence for Length-Biased Survival Data

dc.contributor.authorBentoumi, Rachid
dc.description.abstractIn epidemiological studies, subjects with disease (prevalent cases) differ from newly diseased (incident cases). They tend to survive longer due to sampling bias, and related covariates will also be biased. Methods for regression analyses have recently been proposed to measure the potential effects of covariates on survival. The goal is to extend the dependence measure of Kent (1983), based on the information gain, in the context of length-biased sampling. In this regard, to estimate information gain and dependence measure for length-biased data, we propose two different methods namely kernel density estimation with a regression procedure and parametric copulas. We will assess the consistency for all proposed estimators. Algorithms detailing how to generate length-biased data, using kernel density estimation with regression procedure and parametric copulas approaches, are given. Finally, the performances of the estimated information gain and dependence measure, under length-biased sampling, are demonstrated through simulation studies.
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectLength-biased sampling
dc.subjectCovariate distribution
dc.subjectLength-biased distribution
dc.subjectInformation gain
dc.subjectDependence measure
dc.subjectKernel density estimation
dc.subjectParametric copulas
dc.titleMeasure of Dependence for Length-Biased Survival Data
dc.contributor.supervisorAlvo, Mayer
dc.contributor.supervisorMesfioui, Mhamed / Science
uottawa.departmentMathématiques et statistique / Mathematics and Statistics
CollectionThèses, 2011 - // Theses, 2011 -