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Activity Number: 124
Type: Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #319257
Title: Location and Scale Parameters Testing by Empirical Likelihood Estimation
Author(s): Ningning Wang*
Companies: Jackson State University
Keywords: Density functional ; location and scale parameters ; kernel estimation ; empirical likelihood ; mean square error

A novel class of empirical likelihood nonparametric estimates of density functionals (ELKDFE) is constructed based on kernel density function (KDF) and the concepts of empirical likelihood. These estimates have smaller bias and mean square error than the standard estimates based on KDF. Applications of this to location and scale parameters testing Simulation results show that the empirical likelihood estimates are significantly better than the standard ones for small and moderate sample sizes.

Authors who are presenting talks have a * after their name.

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