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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my.utm.37942017-10-24T06:49:10Z http://eprints.utm.my/id/eprint/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/id/eprint/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 |
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QA Mathematics Shabri, Ani Jemain, Abdul Aziz LQ-Moments: application to the log-normal distribution |
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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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Article |
author |
Shabri, Ani Jemain, Abdul Aziz |
author_facet |
Shabri, Ani Jemain, Abdul Aziz |
author_sort |
Shabri, Ani |
title |
LQ-Moments: application to the log-normal distribution |
title_short |
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_sort |
lq-moments: application to the log-normal distribution |
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Science Publications |
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2006 |
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http://eprints.utm.my/id/eprint/3794/1/AniShabri2006_LQMomentsApplicationtotheLogNormal.pdf http://eprints.utm.my/id/eprint/3794/ http://www.scipub.org/fulltext/jms2/jms223414-421.pdf |
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