LQ-Moments: application to the log-normal distribution

Mudolkar and Hutson (1998) extended L-moments to new moment like entitles called LQmoments (LQMOM). The objective of this paper is to develop improved LQMOM that do not impose restrictions on the value of p and a such as the median, trimean or the Gastwirth but we explore an extended class of LQMOM...

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Main Authors: Shabri, Ani, Jemain, Abdul Aziz
Format: Article
Language:English
Published: Science Publications 2006
Subjects:
Online Access:http://eprints.utm.my/3794/
http://eprints.utm.my/3794/1/AniShabri2006_LQMomentsApplicationtotheLogNormal.pdf
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author Shabri, Ani
Jemain, Abdul Aziz
author_facet Shabri, Ani
Jemain, Abdul Aziz
author_sort Shabri, Ani
building UTeM Institutional Repository
collection Online Access
description Mudolkar and Hutson (1998) extended L-moments to new moment like entitles called LQmoments (LQMOM). The objective of this paper is to develop improved LQMOM that do not impose restrictions on the value of p and a such as the median, trimean or the Gastwirth but we explore an extended 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 will be proposed to estimate the quantile function. Monte Carlo simulations are conducted to illustrate the performance of the proposed estimators of the log-normal 3 (LN3) distribution were compared with the estimators based on conventional LMOM and MOM (method of moments) for various sample sizes and return periods.
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spelling utm-37942017-10-24T06:49:10Z http://eprints.utm.my/3794/ LQ-Moments: application to the log-normal distribution Shabri, Ani Jemain, Abdul Aziz QA Mathematics Mudolkar and Hutson (1998) extended L-moments to new moment like entitles called LQmoments (LQMOM). The objective of this paper is to develop improved LQMOM that do not impose restrictions on the value of p and a such as the median, trimean or the Gastwirth but we explore an extended 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 will be proposed to estimate the quantile function. Monte Carlo simulations are conducted to illustrate the performance of the proposed estimators of the log-normal 3 (LN3) distribution were compared with the estimators based on conventional LMOM and MOM (method of moments) for various sample sizes and return periods. Science Publications 2006 Article PeerReviewed application/pdf en http://eprints.utm.my/3794/1/AniShabri2006_LQMomentsApplicationtotheLogNormal.pdf Shabri, Ani and Jemain, Abdul Aziz (2006) LQ-Moments: application to the log-normal distribution. Journal of Mathematics and Statistics, 2 (3). pp. 414-421. ISSN 1549-3644 http://www.scipub.org/fulltext/jms2/jms223414-421.pdf
spellingShingle QA Mathematics
Shabri, Ani
Jemain, Abdul Aziz
LQ-Moments: application to the log-normal distribution
title LQ-Moments: application to the log-normal distribution
title_full LQ-Moments: application to the log-normal distribution
title_fullStr LQ-Moments: application to the log-normal distribution
title_full_unstemmed LQ-Moments: application to the log-normal distribution
title_short LQ-Moments: application to the log-normal distribution
title_sort lq-moments: application to the log-normal distribution
topic QA Mathematics
url http://eprints.utm.my/3794/
http://eprints.utm.my/3794/
http://eprints.utm.my/3794/1/AniShabri2006_LQMomentsApplicationtotheLogNormal.pdf