Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome
| ISSN: |
1436-3259
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| Source: |
Springer Online Journal Archives 1860-2000
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| Topics: |
Architecture, Civil Engineering, Surveying
Energy, Environment Protection, Nuclear Power Engineering
Geography
Geosciences
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| Notes: |
Abstract The principle of maximum entropy (POME) was employed to derive a new method of parameter estimation for the 3-parameter log-logistic distribution (LLD3). Monte Carlo simulated data were used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). Simulation results showed that POME's performance was superior in predicting quantiles of large recurrence intervals when population skew was greater than or equal to 2.0. In all other cases, POME's performance was comparable to other methods.
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| Type of Medium: |
Electronic Resource
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| URL: |
| _version_ | 1798296238227980289 |
|---|---|
| autor | Singh, V. P. Guo, H. Yu, F. X. |
| autorsonst | Singh, V. P. Guo, H. Yu, F. X. |
| book_url | http://dx.doi.org/10.1007/BF01585596 |
| datenlieferant | nat_lic_papers |
| hauptsatz | hsatz_simple |
| identnr | NLM20597712X |
| issn | 1436-3259 |
| journal_name | Stochastic environmental research and risk assessment |
| materialart | 1 |
| notes | Abstract The principle of maximum entropy (POME) was employed to derive a new method of parameter estimation for the 3-parameter log-logistic distribution (LLD3). Monte Carlo simulated data were used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). Simulation results showed that POME's performance was superior in predicting quantiles of large recurrence intervals when population skew was greater than or equal to 2.0. In all other cases, POME's performance was comparable to other methods. |
| package_name | Springer |
| publikationsjahr_anzeige | 1993 |
| publikationsjahr_facette | 1993 |
| publikationsjahr_intervall | 8009:1990-1994 |
| publikationsjahr_sort | 1993 |
| publisher | Springer |
| reference | 7 (1993), S. 163-177 |
| search_space | articles |
| shingle_author_1 | Singh, V. P. Guo, H. Yu, F. X. |
| shingle_author_2 | Singh, V. P. Guo, H. Yu, F. X. |
| shingle_author_3 | Singh, V. P. Guo, H. Yu, F. X. |
| shingle_author_4 | Singh, V. P. Guo, H. Yu, F. X. |
| shingle_catch_all_1 | Singh, V. P. Guo, H. Yu, F. X. Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome Abstract The principle of maximum entropy (POME) was employed to derive a new method of parameter estimation for the 3-parameter log-logistic distribution (LLD3). Monte Carlo simulated data were used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). Simulation results showed that POME's performance was superior in predicting quantiles of large recurrence intervals when population skew was greater than or equal to 2.0. In all other cases, POME's performance was comparable to other methods. 1436-3259 14363259 Springer |
| shingle_catch_all_2 | Singh, V. P. Guo, H. Yu, F. X. Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome Abstract The principle of maximum entropy (POME) was employed to derive a new method of parameter estimation for the 3-parameter log-logistic distribution (LLD3). Monte Carlo simulated data were used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). Simulation results showed that POME's performance was superior in predicting quantiles of large recurrence intervals when population skew was greater than or equal to 2.0. In all other cases, POME's performance was comparable to other methods. 1436-3259 14363259 Springer |
| shingle_catch_all_3 | Singh, V. P. Guo, H. Yu, F. X. Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome Abstract The principle of maximum entropy (POME) was employed to derive a new method of parameter estimation for the 3-parameter log-logistic distribution (LLD3). Monte Carlo simulated data were used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). Simulation results showed that POME's performance was superior in predicting quantiles of large recurrence intervals when population skew was greater than or equal to 2.0. In all other cases, POME's performance was comparable to other methods. 1436-3259 14363259 Springer |
| shingle_catch_all_4 | Singh, V. P. Guo, H. Yu, F. X. Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome Abstract The principle of maximum entropy (POME) was employed to derive a new method of parameter estimation for the 3-parameter log-logistic distribution (LLD3). Monte Carlo simulated data were used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). Simulation results showed that POME's performance was superior in predicting quantiles of large recurrence intervals when population skew was greater than or equal to 2.0. In all other cases, POME's performance was comparable to other methods. 1436-3259 14363259 Springer |
| shingle_title_1 | Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome |
| shingle_title_2 | Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome |
| shingle_title_3 | Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome |
| shingle_title_4 | Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome |
| sigel_instance_filter | dkfz geomar wilbert ipn albert fhp |
| source_archive | Springer Online Journal Archives 1860-2000 |
| timestamp | 2024-05-06T09:48:55.562Z |
| titel | Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome |
| titel_suche | Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome |
| topic | ZH-ZI ZP R TE-TZ |
| uid | nat_lic_papers_NLM20597712X |