title: Non-parametric hypothesis testing and confidence intervals with doubly censored data.
abstract: The non-parametric maximum likelihood estimator (NPMLE) of the distribution function with doubly censored data can be computed using the self-consistent algorithm (Tumbull, 1974). We extend the self-consistent algorithm to include a constraint on the NPMLE. We then show how to construct confidence intervals and test hypotheses based on the NPMLE via the empirical likelihood ratio. Finally, we present some numerical comparisons of the performance of the above method with another method that makes use of the influence functions.
authors.affiliation: Department of Statistics, University of Kentucky, Lexington, KY 40506-0027, USA. kun.chen.b@bayer.com
authors.forename: Kun
authors.initials: K
authors.lastname: Chen
authors.forename: Mai
authors.initials: M
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D015993:Life Tables; D016013:Likelihood Functions; D012107:Research Design; D012680:Sensitivity and Specificity