Jackknife empirical likelihood ratio test for log symmetric distribution using probability weighted moments

Fuente: arXiv
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Autori principali: S, Anjana, Kattumannil, Sudheesh
Natura: Preprint
Pubblicazione: 2024
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author S, Anjana
Kattumannil, Sudheesh
author_facet S, Anjana
Kattumannil, Sudheesh
contents Log symmetric distributions are useful in modeling data which show high skewness and have found applications in various fields. Using a recent characterization for log symmetric distributions, we propose a goodness of fit test for testing log symmetry. The asymptotic distributions of the test statistics under both null and alternate distributions are obtained. As the normal-based test is difficult to implement, we also propose a jackknife empirical likelihood (JEL) ratio test for testing log symmetry. We conduct a Monte Carlo Simulation to evaluate the performance of the JEL ratio test. Finally, we illustrated our methodology using different data sets.
format Preprint
id arxiv_https___arxiv_org_abs_2410_04082
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Jackknife empirical likelihood ratio test for log symmetric distribution using probability weighted moments
S, Anjana
Kattumannil, Sudheesh
Methodology
Statistics Theory
Log symmetric distributions are useful in modeling data which show high skewness and have found applications in various fields. Using a recent characterization for log symmetric distributions, we propose a goodness of fit test for testing log symmetry. The asymptotic distributions of the test statistics under both null and alternate distributions are obtained. As the normal-based test is difficult to implement, we also propose a jackknife empirical likelihood (JEL) ratio test for testing log symmetry. We conduct a Monte Carlo Simulation to evaluate the performance of the JEL ratio test. Finally, we illustrated our methodology using different data sets.
title Jackknife empirical likelihood ratio test for log symmetric distribution using probability weighted moments
topic Methodology
Statistics Theory
url https://arxiv.org/abs/2410.04082