Trimmed L-moments

Classical estimation methods (least squares, the method of moments and maximum likelihood) work well in regular cases such as the exponential family, but outliers can have undue influence on these methods. We define population trimmed L-moments (TL-moments) and corresponding sample TL-moments as rob...

Full description

Saved in:
Bibliographic Details
Published in:Computational statistics & data analysis 2003-07, Vol.43 (3), p.299-314
Main Authors: Elamir, Elsayed A.H., Seheult, Allan H.
Format: Article
Language:English
Subjects:
Exact sciences and technology
L-moments
Mathematics
Nonparametric inference
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Order statistics
Outliers
Probability and statistics
Robust estimation
Sciences and techniques of general use
Statistics
Trimmed mean
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:Classical estimation methods (least squares, the method of moments and maximum likelihood) work well in regular cases such as the exponential family, but outliers can have undue influence on these methods. We define population trimmed L-moments (TL-moments) and corresponding sample TL-moments as robust generalisations of population and sample L-moments. TL-moments assign zero weight to extreme observations, they are easy to compute, their sample variances and covariances can be obtained in closed form, and they are more robust than L-moments are to the presence of outliers. Moreover, a population TL-moment may be well defined where the corresponding population L-moment does not exist: for example, the first population TL-moment is well defined for a Cauchy distribution, but the first population L-moment, the population mean, does not exist. The sample TL-mean is compared with other robust estimators of location.
ISSN:0167-9473
1872-7352
DOI:10.1016/S0167-9473(02)00250-5