scholarly journals On maximum likelihood estimation of the extreme value index

2004 ◽  
Vol 14 (3) ◽  
pp. 1179-1201 ◽  
Author(s):  
Holger Drees ◽  
Ana Ferreira ◽  
Laurens de Haan
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Extreme Value Index

scholarly journals On the Maximum Likelihood Estimation of Extreme Value Index Based on k-Record Values

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Abderrahim Louzaoui ◽  
Mohamed El Arrouchi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Estimator ◽  
Likelihood Estimation ◽  
Record Values ◽  
Extreme Value ◽  
Extreme Value Index ◽  
Likelihood Estimator ◽  
Consistent Solution ◽  
Strongly Consistent

In this paper, we study the existence and consistency of the maximum likelihood estimator of the extreme value index based on k-record values. Following the method used by Drees et al. (2004) and Zhou (2009), we prove that the likelihood equations, in terms of k-record values, eventually admit a strongly consistent solution without any restriction on the extreme value index, which is not the case in the aforementioned studies.

CHOOSING THE BEST FIT DISTRIBUTION FOR RAINFALL EVENT CHARACTERISTICS BASED ON 6H-IETD WITHIN PENINSULAR MALAYSIA

2015 ◽  
Vol 75 (1) ◽  
Author(s):  
Zulkarnain Hassan ◽  
Supiah Shamsudin ◽  
Sobri Harun
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Peninsular Malaysia ◽  
Rainfall Event ◽  
Distribution Model ◽  
Event Time ◽  
Generalized Pareto ◽  
Best Fit

In selecting the best-fit distribution model for the rainfall event characteristics based on the inter-event time definition (IETD) of 6 hours for the selected rainfall in the Peninsular of Malaysia, seven distributions were utilized namely the beta (B4), exponential (EX1), gamma (G2), generalized extreme value (GEV), generalized Pareto (GP), Log-Pearson 3 (LP3), and Wakeby (WKB). Maximum likelihood estimation (MLE) was applied to estimate the parameters of each distribution.  Based on the results, GP, WKB and GEV were found to be the most suitable distribution for describing the rainfall event characteristics in the studied regions.  

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