Nonparametric Kernel estimation of annual maximum stream flow quantiles.

A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show...

Full description

Bibliographic Details
Main Author: Shabri, Ani
Format: Article
Language:English
Published: Department of Mathematics, Faculty of Science 2002
Subjects:
Online Access:http://eprints.utm.my/8811/
http://eprints.utm.my/8811/1/AniShabri2002_NonparametricKernelEstimationofAnnual.pdf
_version_ 1848891771741798400
author Shabri, Ani
author_facet Shabri, Ani
author_sort Shabri, Ani
building UTeM Institutional Repository
collection Online Access
description A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show that quantiles estimated by nonparametric method using these techniques have small root mean square error and root mean absolute error. Based on correlation coefficient test shown that the nonparametric model approach is accurate, uniform and flexible alternatives to parametric models for flood frequency analysis.
first_indexed 2025-11-15T21:03:16Z
format Article
id utm-8811
institution Universiti Teknologi Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T21:03:16Z
publishDate 2002
publisher Department of Mathematics, Faculty of Science
recordtype eprints
repository_type Digital Repository
spelling utm-88112010-06-02T01:58:08Z http://eprints.utm.my/8811/ Nonparametric Kernel estimation of annual maximum stream flow quantiles. Shabri, Ani QA Mathematics A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show that quantiles estimated by nonparametric method using these techniques have small root mean square error and root mean absolute error. Based on correlation coefficient test shown that the nonparametric model approach is accurate, uniform and flexible alternatives to parametric models for flood frequency analysis. Department of Mathematics, Faculty of Science 2002-12 Article PeerReviewed application/pdf en http://eprints.utm.my/8811/1/AniShabri2002_NonparametricKernelEstimationofAnnual.pdf Shabri, Ani (2002) Nonparametric Kernel estimation of annual maximum stream flow quantiles. Matematika, 18 (2). pp. 99-107. ISSN 0127-8274 http://portal.psz.utm.my/psz/index.php?option=com_content&task=view&id=128&Itemid=305&PHPSESSID=81b664e998055f65b4ccff8f61bf7cb2
spellingShingle QA Mathematics
Shabri, Ani
Nonparametric Kernel estimation of annual maximum stream flow quantiles.
title Nonparametric Kernel estimation of annual maximum stream flow quantiles.
title_full Nonparametric Kernel estimation of annual maximum stream flow quantiles.
title_fullStr Nonparametric Kernel estimation of annual maximum stream flow quantiles.
title_full_unstemmed Nonparametric Kernel estimation of annual maximum stream flow quantiles.
title_short Nonparametric Kernel estimation of annual maximum stream flow quantiles.
title_sort nonparametric kernel estimation of annual maximum stream flow quantiles.
topic QA Mathematics
url http://eprints.utm.my/8811/
http://eprints.utm.my/8811/
http://eprints.utm.my/8811/1/AniShabri2002_NonparametricKernelEstimationofAnnual.pdf