LQ-moment: application to the generalized extreme value
The LQ-moments are analogous to L-moments, found always exists, easier to compute and have the same potential as L-moment were re-visited. The efficiency of the Weighted Kernal Quantile (WKQ), HD (Harrell and Davis) quantile the weighted HD quantiles estimators compared with the Linear Interpolation...
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| Format: | Article |
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Asian Network for Scientific Information
2007
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| _version_ | 1825910071250386944 |
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| author | Shabri, Ani Jemain, Abdul Aziz |
| author_facet | Shabri, Ani Jemain, Abdul Aziz |
| author_sort | Shabri, Ani |
| collection | ePrints |
| description | The LQ-moments are analogous to L-moments, found always exists, easier to compute and have the same potential as L-moment were re-visited. The efficiency of the Weighted Kernal Quantile (WKQ), HD (Harrell and Davis) quantile the weighted HD quantiles estimators compared with the Linear Interpolation Quantile (LIQ) estimator to estimate the sample of the LQ-moments. In this study we discuss of the quantile estimator of the LQ-moments method to estimate the parameters of the Generalized Extreme Value (GEV) distribution. In order to determine which quantile estimator is the most suitable for the LQ-moment, the Monte Carlo simulation was considered. The result shows that the WKQ is considered as the best quantile estimator compared with the HDWQ, HDQ and LIQ estimator. |
| first_indexed | 2024-03-05T18:11:37Z |
| format | Article |
| id | utm.eprints-7645 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:11:37Z |
| publishDate | 2007 |
| publisher | Asian Network for Scientific Information |
| record_format | dspace |
| spelling | utm.eprints-76452009-05-12T07:06:44Z http://eprints.utm.my/7645/ LQ-moment: application to the generalized extreme value Shabri, Ani Jemain, Abdul Aziz QA Mathematics The LQ-moments are analogous to L-moments, found always exists, easier to compute and have the same potential as L-moment were re-visited. The efficiency of the Weighted Kernal Quantile (WKQ), HD (Harrell and Davis) quantile the weighted HD quantiles estimators compared with the Linear Interpolation Quantile (LIQ) estimator to estimate the sample of the LQ-moments. In this study we discuss of the quantile estimator of the LQ-moments method to estimate the parameters of the Generalized Extreme Value (GEV) distribution. In order to determine which quantile estimator is the most suitable for the LQ-moment, the Monte Carlo simulation was considered. The result shows that the WKQ is considered as the best quantile estimator compared with the HDWQ, HDQ and LIQ estimator. Asian Network for Scientific Information 2007-01-01 Article PeerReviewed Shabri, Ani and Jemain, Abdul Aziz (2007) LQ-moment: application to the generalized extreme value. Journal of Applied Sciences, 7 (1). pp. 115-120. ISSN 1812-5654 http://dx.doi.org/10.3923/jas.2007.115.120 10.3923/jas.2007.115.120 |
| spellingShingle | QA Mathematics Shabri, Ani Jemain, Abdul Aziz LQ-moment: application to the generalized extreme value |
| title | LQ-moment: application to the generalized extreme value |
| title_full | LQ-moment: application to the generalized extreme value |
| title_fullStr | LQ-moment: application to the generalized extreme value |
| title_full_unstemmed | LQ-moment: application to the generalized extreme value |
| title_short | LQ-moment: application to the generalized extreme value |
| title_sort | lq moment application to the generalized extreme value |
| topic | QA Mathematics |
| work_keys_str_mv | AT shabriani lqmomentapplicationtothegeneralizedextremevalue AT jemainabdulaziz lqmomentapplicationtothegeneralizedextremevalue |