Return to search

A New Jackknife Empirical Likelihood Method for U-Statistics

U-statistics generalizes the concept of mean of independent identically distributed (i.i.d.) random variables and is widely utilized in many estimating and testing problems. The standard empirical likelihood (EL) for U-statistics is computationally expensive because of its onlinear constraint. The jackknife empirical likelihood method largely relieves computation burden by circumventing the construction of the nonlinear constraint. In this thesis, we adopt a new jackknife empirical likelihood method to make inference for the general volume under the ROC surface (VUS), which is one typical kind of U-statistics. Monte Carlo simulations are conducted to show that the EL confidence intervals perform well in terms of the coverage probability and average length for various sample sizes.

Identiferoai:union.ndltd.org:GEORGIA/oai:digitalarchive.gsu.edu:math_theses-1096
Date25 April 2011
CreatorsMa, Zhengbo
PublisherDigital Archive @ GSU
Source SetsGeorgia State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceMathematics Theses

Page generated in 0.0018 seconds