Fitting the generalized logistic distribution by LQ-moments
The method of LQ-moments (LQMOM) for estimating parameters and quantiles of the Generalized Logistic (GL) distribution are introduced. We explore and extend class of LQMOM with consideration combinations of p and a values in the range 0 and 0.5. The popular quantile estimator namely the weighted ker...
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| Main Authors: | , |
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| Format: | Article |
| Published: |
Springer New York
2011
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/44928/ |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | The method of LQ-moments (LQMOM) for estimating parameters and quantiles of the Generalized Logistic (GL) distribution are introduced. We explore and extend class of LQMOM with consideration combinations of p and a values in the range 0 and 0.5. The popular quantile estimator namely the weighted kernel quantile (WKQ) estimator is proposed to estimate the quantile function. A comparison of these methods is done by simulation. The performances of the proposed estimators of the GL distribution was compared with the estimators based on L-moments for various sample sizes and return periods. The overall results show the LQMOM provides better results only for small or moderate sample size. |
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