Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416681 | Computational Statistics & Data Analysis | 2006 | 17 Pages |
Abstract
Let X and Y be two independent continuous random variables. Three techniques to obtain confidence intervals for ρ=Pr{Y>X}ρ=Pr{Y>X} are discussed in a partially parametric framework. One method relies on the asymptotic normality of an estimator for ρρ; the remaining methods involve empirical likelihood and combine it with maximum likelihood estimation and with full parametric likelihood, respectively. Finite-sample accuracy of the confidence intervals is assessed through a simulation study. An illustration is given using a data set on the detection of carriers of Duchenne Muscular Dystrophy.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Gianfranco Adimari, Monica Chiogna,