Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jeffrey Miecznikowski, Albert Vexler, Lori Shepherd
Title: dbEmpLikeGOF: An R Package for Nonparametric Likelihood Ratio Tests for Goodness-of-Fit and Two-Sample Comparisons Based on Sample Entropy
Abstract: We introduce and examine dbEmpLikeGOF, an R package for performing goodness-of-fit tests based on sample entropy. This package also performs the two sample distribution comparison test. For a given vector of data observations, the provided function dbEmpLikeGOF tests the data for the proposed null distributions, or tests for distribution equality between two vectors of observations. The proposed methods represent a distribution-free density-based empirical likelihood technique applied to nonparametric testing. The proposed procedure performs exact and very efficient p values for each test statistic obtained from a Monte Carlo (MC) resampling scheme. Note by using an MC scheme, we are assured exact level α tests that approximate nonparametrically most powerful Neyman-Pearson decision rules. Although these entropy based tests are known in the theoretical literature to be very efficient, they have not been well addressed in statistical software. This article briefly presents the proposed tests and introduces the package, with applications to real data. We apply the methods to produce a novel analysis of a recently published dataset related to coronary heart disease.

Page views:: 3110. Submitted: 2011-03-02. Published: 2013-09-03.
Paper: dbEmpLikeGOF: An R Package for Nonparametric Likelihood Ratio Tests for Goodness-of-Fit and Two-Sample Comparisons Based on Sample Entropy     Download PDF (Downloads: 2868)
Supplements:
dbEmpLikeGOF_1.2.4.tar.gz: R source package Download (Downloads: 283; 359KB)
v54i03.R: R example code from the paper Download (Downloads: 339; 22KB)
v54i03-data.zip: Example data sets and simulation results Download (Downloads: 279; 4MB)

DOI: 10.18637/jss.v054.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.