More on the four-parameter kappa distribution

The generalized extreme-value has been the distribution of choice for modeling available maxima (or minima) data since theory has shown it to be the limiting form of the distribution of extremes. However, fits to finite samples are not always adequate. Hosking (1994) and Parida (1999) suggest the fo...

Full description

Saved in:
Bibliographic Details
Published in:Journal of statistical computation and simulation 2001-11, Vol.71 (2), p.99-113
Main Authors: Dupuis, D.J., Winchester, C.
Format: Article
Language:English
Subjects:
Exact sciences and technology
Feasibility
Four-parameter Kappa distribution
Generalized extreme-value distribution
Mathematics
Maximum likelihood
Nonparametric inference
Probability and statistics
Sciences and techniques of general use
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The generalized extreme-value has been the distribution of choice for modeling available maxima (or minima) data since theory has shown it to be the limiting form of the distribution of extremes. However, fits to finite samples are not always adequate. Hosking (1994) and Parida (1999) suggest the four-parameter Kappa distribution as an alternative. Hosking (1994) developed an L-moment procedure for estimation. Some compromises must be made in practice however, as seen in Parida (1999). L-moment estimators of the four-parameter Kappa distribution are not always computable nor feasible. A simulation study in this paper quantifies the extent of each problem. Maximum likelihood is investigated as an alternative method of estimation and a simulation study compares the performance of both methods of estimation. Finally, further benefits of maximum likelihood are shown when wind speeds From the Tropical Pacific are examined and the weekly maxima for 10 buoys in the area are analyzed.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949650108812137