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The L2E method
Author(s) -
Scott David W.
Publication year - 2009
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.4
Subject(s) - estimator , computer science , outlier , maximum likelihood , bayesian probability , regularization (linguistics) , software , mathematical optimization , mathematics , statistics , artificial intelligence , programming language
Estimation theory and practice is generally focused on maximum likelihood methodology, which boasts of claims of efficiency and widespread availability in software. Likelihood methods occasionally encounter problems with small sample sizes, or if the data are contaminated with outliers. The first problem can be addressed by regularization methods such as Bayesian estimation; the latter problem can be solved by using robust methodology such as the M ‐estimator, for example. \documentclass{article}\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{amsfonts}\pagestyle{empty}\begin{document}$\newcommand{\LTE}{L\!_{\textstyle\textit 2}\!E} \LTE$\end{document} is a particular example of an M ‐estimator. This article motivates its special properties and provides detailed examples that take advantage of those properties. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Density Estimation