Local Asymptotic Normality in Extreme Value Index Estimation

1997 ◽  
Vol 49 (4) ◽  
pp. 645-666 ◽  
Author(s):  
Frank Marohn
Keyword(s):  
Asymptotic Normality ◽  
Extreme Value ◽  
Extreme Value Index ◽  
Local Asymptotic Normality ◽  
Index Estimation

Mean-of-order p reduced-bias extreme value index estimation under a third-order framework

Extremes ◽  
2016 ◽  
Vol 19 (4) ◽  
pp. 561-589 ◽  
Author(s):  
Frederico Caeiro ◽  
M. Ivette Gomes ◽  
Jan Beirlant ◽  
Tertius de Wet
Keyword(s):  
Extreme Value ◽  
Extreme Value Index ◽  
Third Order ◽  
Index Estimation

scholarly journals On Extreme Value Index Estimation under Random Censoring

2018 ◽  
Vol 5 (2) ◽  
pp. 419-445
Author(s):  
Richard Minkah ◽  
Tertius de Wet ◽  
Kwabena Doku-Amponsah
Keyword(s):  
Extreme Value ◽  
Extreme Value Index ◽  
Random Censoring ◽  
Index Estimation

scholarly journals Tail product-limit process for truncated data with application to extreme value index estimation

Extremes ◽  
2016 ◽  
Vol 19 (2) ◽  
pp. 219-251 ◽  
Author(s):  
Souad Benchaira ◽  
Djamel Meraghni ◽  
Abdelhakim Necir
Keyword(s):  
Extreme Value ◽  
Extreme Value Index ◽  
Truncated Data ◽  
Limit Process ◽  
Product Limit ◽  
Index Estimation

scholarly journals Extreme Value Index Estimation by Means of an Inequality Curve

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1834
Author(s):  
Emanuele Taufer ◽  
Flavio Santi ◽  
Pier Luigi Novi Inverardi ◽  
Giuseppe Espa ◽  
Maria Michela Dickson
Keyword(s):  
Real Data ◽  
Extreme Value ◽  
Extreme Value Index ◽  
Data Sets ◽  
Powerful Method ◽  
Estimation Strategy ◽  
Graphical Interpretation ◽  
Regularly Varying ◽  
Index Estimation

A characterizing property of Zenga (1984) inequality curve is exploited in order to develop an estimator for the extreme value index of a distribution with regularly varying tail. The approach proposed here has a nice graphical interpretation which provides a powerful method for the analysis of the tail of a distribution. The properties of the proposed estimation strategy are analysed theoretically and by means of simulations. The usefulness of the method will be tested also on real data sets.

On the asymptotic normality of the extreme value index for right-truncated data

2015 ◽  
Vol 107 ◽  
pp. 378-384 ◽  
Author(s):  
Souad Benchaira ◽  
Djamel Meraghni ◽  
Abdelhakim Necir
Keyword(s):  
Asymptotic Normality ◽  
Extreme Value ◽  
Extreme Value Index ◽  
Truncated Data

Lehmer's mean-of-order-p extreme value index estimation: a simulation study and applications

2019 ◽  
Vol 47 (13-15) ◽  
pp. 2825-2845 ◽  
Author(s):  
Helena Penalva ◽  
M. Ivette Gomes ◽  
Frederico Caeiro ◽  
M. Manuela Neves
Keyword(s):  
Simulation Study ◽  
Extreme Value ◽  
Extreme Value Index ◽  
Index Estimation

Regional extreme value index estimation and a test of tail homogeneity

2015 ◽  
Vol 27 (2) ◽  
pp. 103-115 ◽  
Author(s):  
Paul Kinsvater ◽  
Roland Fried ◽  
Jona Lilienthal
Keyword(s):  
Extreme Value ◽  
Extreme Value Index ◽  
Index Estimation

scholarly journals Reduced-Bias Location-Invariant Extreme Value Index Estimation: A Simulation Study

2011 ◽  
Vol 40 (3) ◽  
pp. 424-447 ◽  
Author(s):  
M. Ivette Gomes ◽  
Lígia Henriques-Rodrigues ◽  
M. Cristina Miranda
Keyword(s):  
Simulation Study ◽  
Extreme Value ◽  
Extreme Value Index ◽  
Index Estimation

Optimal choice of kn-records in the extreme value index estimation

2005 ◽  
Vol 23 (2/2005) ◽  
Author(s):  
Mohamed El Arrouchi ◽  
Abdelouahid Imlahi
Keyword(s):  
Extreme Value ◽  
Optimal Choice ◽  
Extreme Value Index ◽  
Index Estimation
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