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Computing least trimmed squares regression with the forward search

By Anthony C. Atkinson and T.-C. Cheng

Abstract

Least trimmed squares (LTS) provides a parametric family of high breakdown estimators in regression with better asymptotic properties than least median of squares (LMS) estimators. We adapt the forward search algorithm of Atkinson (1994) to LTS and provide methods for determining the amount of data to be trimmed. We examine the efficiency of different trimming proportions by simulation and demonstrate the increasing efficiency of parameter estimation as larger proportions of data are fitted using the LTS criterion. Some standard data examples are analysed. One shows that LTS provides more stable solutions than LMS

Topics: QA Mathematics
Publisher: Springer Netherlands
Year: 1999
DOI identifier: 10.1023/A:1008942604045
OAI identifier: oai:eprints.lse.ac.uk:7611
Provided by: LSE Research Online
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