JSM 2005 - Toronto

Abstract #303225

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 265
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #303225
Title: Inference for Very Heavy-tailed Distributions Using Trimmed L-moments
Author(s): J. R. M. Hosking*+
Companies: IBM
Address: T J Watson Research Center, Yorktown Heights, NY, 10598, United States
Keywords: L-moments ; skewness ; kurtosis ; estimation ; summary statistics
Abstract:

Some data such as internet traffic volumes have probability distributions with such heavy tails that the mean may not exist. Trimmed L-moments, introduced by Elamir and Seheult, have promise as summary statistics and parameter estimators for heavy-tailed data. They are certain linear combinations of expected order statistics that give zero weight to the extreme order statistics. I discuss some alternative definitions of trimmed L-moments, give bounds on measures of skewness and kurtosis based on them, and give examples of how trimmed L-moments can be used to identify distributions and estimate frequencies of extreme events for very heavy-tailed data.

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