LQ-moments: application to the extreme value type I distribution
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 quantil...
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Asian Network for Scientific Information
2006
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| Online Access: | http://eprints.utm.my/9051/ http://dx.doi.org/10.3923/jas.2006.993.997 |
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| author | Shabri, Ani Jemain, Abdul Aziz |
| author_facet | Shabri, Ani Jemain, Abdul Aziz |
| author_sort | Shabri, Ani |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.utm.eprints-9051 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2006 |
| publisher | Asian Network for Scientific Information |
| record_format | eprints |
| spelling | my.utm.eprints-90512018-03-22T08:33:46Z http://eprints.utm.my/9051/ LQ-moments: application to the extreme value type I distribution Shabri, Ani Jemain, Abdul Aziz Q Science (General) 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. Asian Network for Scientific Information 2006 Article PeerReviewed Shabri, Ani and Jemain, Abdul Aziz (2006) LQ-moments: application to the extreme value type I distribution. Journal of Applied Sciences, 6 (5). pp. 993-997. ISSN 1812-5654 http://dx.doi.org/10.3923/jas.2006.993.997 10.3923/jas.2006.993.997 |
| spellingShingle | Q Science (General) Shabri, Ani Jemain, Abdul Aziz LQ-moments: application to the extreme value type I distribution |
| title | LQ-moments: application to the extreme value type I distribution |
| title_full | LQ-moments: application to the extreme value type I distribution |
| title_fullStr | LQ-moments: application to the extreme value type I distribution |
| title_full_unstemmed | LQ-moments: application to the extreme value type I distribution |
| title_short | LQ-moments: application to the extreme value type I distribution |
| title_sort | lq-moments: application to the extreme value type i distribution |
| topic | Q Science (General) |
| url | http://eprints.utm.my/9051/ http://dx.doi.org/10.3923/jas.2006.993.997 |
| url_provider | http://eprints.utm.my/ |