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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| Main Authors: | , |
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| Format: | Article |
| Published: |
Asian Network for Scientific Information
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/7645/ http://dx.doi.org/10.3923/jas.2007.115.120 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | 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. |
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