Cover Image

Fitting the generalized logistic distribution by LQ-moments

The method of LQ-moments (LQMOM) for estimating parameters and quantiles of the Generalized Logistic (GL) distribution are introduced. We explore and extend class of LQMOM with consideration combinations of p and a values in the range 0 and 0.5. The popular quantile estimator namely the weighted ker...

Full description

Saved in:
Bibliographic Details
Main Authors: Shabri, Ani, Jemain, Abdul Aziz
Format: Article
Published: Springer New York 2011
Subjects:
U Military Science
Online Access:http://eprints.utm.my/id/eprint/44928/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.44928
record_format eprints
spelling my.utm.449282017-01-31T06:58:41Z http://eprints.utm.my/id/eprint/44928/ Fitting the generalized logistic distribution by LQ-moments Shabri, Ani Jemain, Abdul Aziz U Military Science The method of LQ-moments (LQMOM) for estimating parameters and quantiles of the Generalized Logistic (GL) distribution are introduced. We explore and extend class of LQMOM with consideration combinations of p and a values in the range 0 and 0.5. The popular quantile estimator namely the weighted kernel quantile (WKQ) estimator is proposed to estimate the quantile function. A comparison of these methods is done by simulation. The performances of the proposed estimators of the GL distribution was compared with the estimators based on L-moments for various sample sizes and return periods. The overall results show the LQMOM provides better results only for small or moderate sample size. Springer New York 2011 Article PeerReviewed Shabri, Ani and Jemain, Abdul Aziz (2011) Fitting the generalized logistic distribution by LQ-moments. Applied Mathematical Sciences, 5 (53-56). pp. 2663-2676. ISSN 1312-885X
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic U Military Science
spellingShingle U Military Science
Shabri, Ani
Jemain, Abdul Aziz
Fitting the generalized logistic distribution by LQ-moments
description The method of LQ-moments (LQMOM) for estimating parameters and quantiles of the Generalized Logistic (GL) distribution are introduced. We explore and extend class of LQMOM with consideration combinations of p and a values in the range 0 and 0.5. The popular quantile estimator namely the weighted kernel quantile (WKQ) estimator is proposed to estimate the quantile function. A comparison of these methods is done by simulation. The performances of the proposed estimators of the GL distribution was compared with the estimators based on L-moments for various sample sizes and return periods. The overall results show the LQMOM provides better results only for small or moderate sample size.
format Article
author Shabri, Ani
Jemain, Abdul Aziz
author_facet Shabri, Ani
Jemain, Abdul Aziz
author_sort Shabri, Ani
title Fitting the generalized logistic distribution by LQ-moments
title_short Fitting the generalized logistic distribution by LQ-moments
title_full Fitting the generalized logistic distribution by LQ-moments
title_fullStr Fitting the generalized logistic distribution by LQ-moments
title_full_unstemmed Fitting the generalized logistic distribution by LQ-moments
title_sort fitting the generalized logistic distribution by lq-moments
publisher Springer New York
publishDate 2011
url http://eprints.utm.my/id/eprint/44928/
_version_ 1643651589060165632
score 13.211869

Similar Items