Computing least trimmed squares regression with the forward search

被引：19

作者：

Atkinson, AC ^{[1
]}

Cheng, TC ^{[1
]}

机构：

[1] London Sch Econ, Dept Stat, London WC2A 2AE, England

来源：

STATISTICS AND COMPUTING | 1999年 / 9卷 / 04期

关键词：

detection of outliers; forward search algorithm; high breakdown point; least trimmed squares; regression diagnostics; stalactite plot;

D O I：

10.1023/A:1008942604045

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：251 / 263

页数：13

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