A Test of Symmetry Based on the Kernel Kullback-Leibler Information with Application to Base Deficit Data

Hani M. Samawi; Robert L. Vogel

doi:10.15406/bbij.2016.03.00060

A Test of Symmetry Based on the Kernel Kullback-Leibler Information with Application to Base Deficit Data

Hani M. Samawi, Robert L. Vogel

Georgia Southern University

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Abstract

The assumption of the symmetry of the underlying distribution is important to many statistical inference and modeling procedures. This paper provides a test of symmetry using kernel density estimation and the Kullback-Leibler information. Based on simulation studies, the new test procedure outperforms other tests of symmetry found in the literature, including the Runs Test of Symmetry. We illustrate our new procedure using real data.

Original language	American English
Journal	Biometrics and Biostatistics International Journal
Volume	3
DOIs	https://doi.org/10.15406/bbij.2016.03.00060
State	Published - Jan 27 2016

Disciplines

Biostatistics
Community Health
Public Health

Keywords

Test of symmetry
Power of the test
Overlap coefficients
Kernel Density estimation
Kullback-Leibler information
Nonparametric Test
Kullback-Leibler

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.15406/bbij.2016.03.00060License: CC BY

A Test of Symmetry Based on the Kernel Kullback-Leibler InformatiFinal published version, 950 KBLicense: CC BY

Cite this

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title = "A Test of Symmetry Based on the Kernel Kullback-Leibler Information with Application to Base Deficit Data",

abstract = " The assumption of the symmetry of the underlying distribution is important to many statistical inference and modeling procedures. This paper provides a test of symmetry using kernel density estimation and the Kullback-Leibler information. Based on simulation studies, the new test procedure outperforms other tests of symmetry found in the literature, including the Runs Test of Symmetry. We illustrate our new procedure using real data.",

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language = "American English",

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T1 - A Test of Symmetry Based on the Kernel Kullback-Leibler Information with Application to Base Deficit Data

AU - Samawi, Hani M.

AU - Vogel, Robert L.

PY - 2016/1/27

Y1 - 2016/1/27

N2 - The assumption of the symmetry of the underlying distribution is important to many statistical inference and modeling procedures. This paper provides a test of symmetry using kernel density estimation and the Kullback-Leibler information. Based on simulation studies, the new test procedure outperforms other tests of symmetry found in the literature, including the Runs Test of Symmetry. We illustrate our new procedure using real data.

AB - The assumption of the symmetry of the underlying distribution is important to many statistical inference and modeling procedures. This paper provides a test of symmetry using kernel density estimation and the Kullback-Leibler information. Based on simulation studies, the new test procedure outperforms other tests of symmetry found in the literature, including the Runs Test of Symmetry. We illustrate our new procedure using real data.

KW - Test of symmetry

KW - Power of the test

KW - Overlap coefficients

KW - Kernel Density estimation

KW - Kullback-Leibler information

KW - Nonparametric Test

KW - Kullback-Leibler

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpubs/144

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpubs/219

UR - https://doi.org/10.15406/bbij.2016.03.00060

U2 - 10.15406/bbij.2016.03.00060

DO - 10.15406/bbij.2016.03.00060

M3 - Article

VL - 3

JO - Biometrics and Biostatistics International Journal

JF - Biometrics and Biostatistics International Journal

ER -

A Test of Symmetry Based on the Kernel Kullback-Leibler Information with Application to Base Deficit Data

Abstract

Disciplines

Keywords

UN SDGs

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