LQ-moments

Application to the Extreme Value type I distribution

Ani Bin Shabri, Abdul Aziz Jemain

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The objective of this study is to develop improved LQ-moments that do not impose restrictions on the value of p and α such as the median, trimean or the Gastwirth but we explore an extended class of LQMOM with consideration combinations of p and α 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. The performances of the proposed estimators of the Extreme Values Type 1 (EV1) distribution were compared with the estimators based on conventional LMOM, MOM (method of moments), ML (method of maximum likelihood) and the LQ-moments based on LIQ (linear interpolation quantile) for various sample sizes and return periods.

Original languageEnglish
Pages (from-to)993-997
Number of pages5
JournalJournal of Applied Sciences
Volume6
Issue number5
DOIs
Publication statusPublished - May 2006

Fingerprint

Extreme Values
Quantile
Moment
Estimator
Quantile Function
Moment Method
Linear Interpolation
Method of Moments
Maximum Likelihood
Sample Size
kernel
Restriction
Estimate
Range of data

Keywords

  • L-moments
  • Linear interpolation quantile
  • LQ-moments
  • Quik estimator
  • The weighted kernel quantile

ASJC Scopus subject areas

  • General

Cite this

LQ-moments : Application to the Extreme Value type I distribution. / Shabri, Ani Bin; Jemain, Abdul Aziz.

In: Journal of Applied Sciences, Vol. 6, No. 5, 05.2006, p. 993-997.

Research output: Contribution to journalArticle

Shabri, Ani Bin ; Jemain, Abdul Aziz. / LQ-moments : Application to the Extreme Value type I distribution. In: Journal of Applied Sciences. 2006 ; Vol. 6, No. 5. pp. 993-997.
@article{ad8cbaf2097c46cfb606bfc8cec5f299,
title = "LQ-moments: Application to the Extreme Value type I distribution",
abstract = "The objective of this study is to develop improved LQ-moments that do not impose restrictions on the value of p and α such as the median, trimean or the Gastwirth but we explore an extended class of LQMOM with consideration combinations of p and α 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. The performances of the proposed estimators of the Extreme Values Type 1 (EV1) distribution were compared with the estimators based on conventional LMOM, MOM (method of moments), ML (method of maximum likelihood) and the LQ-moments based on LIQ (linear interpolation quantile) for various sample sizes and return periods.",
keywords = "L-moments, Linear interpolation quantile, LQ-moments, Quik estimator, The weighted kernel quantile",
author = "Shabri, {Ani Bin} and Jemain, {Abdul Aziz}",
year = "2006",
month = "5",
doi = "10.3923/jas.2006.993.997",
language = "English",
volume = "6",
pages = "993--997",
journal = "Journal of Applied Sciences",
issn = "1812-5654",
publisher = "Asian Network for Scientific Information",
number = "5",

}

TY - JOUR

T1 - LQ-moments

T2 - Application to the Extreme Value type I distribution

AU - Shabri, Ani Bin

AU - Jemain, Abdul Aziz

PY - 2006/5

Y1 - 2006/5

N2 - The objective of this study is to develop improved LQ-moments that do not impose restrictions on the value of p and α such as the median, trimean or the Gastwirth but we explore an extended class of LQMOM with consideration combinations of p and α 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. The performances of the proposed estimators of the Extreme Values Type 1 (EV1) distribution were compared with the estimators based on conventional LMOM, MOM (method of moments), ML (method of maximum likelihood) and the LQ-moments based on LIQ (linear interpolation quantile) for various sample sizes and return periods.

AB - The objective of this study is to develop improved LQ-moments that do not impose restrictions on the value of p and α such as the median, trimean or the Gastwirth but we explore an extended class of LQMOM with consideration combinations of p and α 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. The performances of the proposed estimators of the Extreme Values Type 1 (EV1) distribution were compared with the estimators based on conventional LMOM, MOM (method of moments), ML (method of maximum likelihood) and the LQ-moments based on LIQ (linear interpolation quantile) for various sample sizes and return periods.

KW - L-moments

KW - Linear interpolation quantile

KW - LQ-moments

KW - Quik estimator

KW - The weighted kernel quantile

UR - http://www.scopus.com/inward/record.url?scp=33745766129&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745766129&partnerID=8YFLogxK

U2 - 10.3923/jas.2006.993.997

DO - 10.3923/jas.2006.993.997

M3 - Article

VL - 6

SP - 993

EP - 997

JO - Journal of Applied Sciences

JF - Journal of Applied Sciences

SN - 1812-5654

IS - 5

ER -