Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323439 
Year of Publication: 
2024
Citation: 
[Journal:] Croatian Review of Economic, Business and Social Statistics (CREBSS) [ISSN:] 2459-5616 [Volume:] 10 [Issue:] 2 [Year:] 2024 [Pages:] 29-48
Publisher: 
Croatian Statistical Association (CSA), Zagreb
Abstract: 
Studying the extreme value theory (EVT) involves multiple main objectives, among them the estimation of the tail index parameter. Some estimation methods are used to estimate the tail index parameter like maximum likelihood estimation (MLE). Additionally, the Hill estimator is one type of maximum likelihood estimator, which is a more robust with a large sample than a small sample. This research proposes the construction of an alternative estimator for the parameter of the heavy-tailed distribution using the maximum lq-likelihood estimation (MLqE) approach in order to adapt the ML and Hill estimator with the small sample. Furthermore, the maximum lq-likelihood estimator asymptotic normality is established. Moreover, several simulation studies in order to compare the MLq estimator with the ML estimators are provided. In the excesses over high suitable threshold values the number of the largest observation k will lead to an efficient estimate of the Hill estimator. For this, selection of k in the Hill estimator was investigated using the method of the quantile type 8 which is effective with the hydrology data. The performance of the Hill estimator and the lq-Hill estimator is subsequently compared by employing real relies with the distribution of hydrology data.
Subjects: 
excesses over threshold
extreme value index
heavy-tailed distribution
maximum lq-likelihood estimator
JEL: 
C13
C46
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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