A practical approximation algorithm for the LMS line estimator

The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-of-squares (LMS) regression line estimator is among...

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Veröffentlicht in:	Computational statistics & data analysis Jg. 51; H. 5; S. 2461 - 2486
Hauptverfasser:	Mount, David M., Netanyahu, Nathan S., Romanik, Kathleen, Silverman, Ruth, Wu, Angela Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.02.2007 Elsevier Science Elsevier
Schriftenreihe:	Computational Statistics & Data Analysis
Schlagworte:	Approximation algorithms Calculus of variations and optimal control Exact sciences and technology General topics Least median-of-squares regression Line arrangements Line fitting Mathematical analysis Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Randomized algorithms Robust estimation Sciences and techniques of general use Statistics Approximation algorithms Randomized algorithms Robust estimation Line fitting Line arrangements Least median-of-squares regression Data analysis Approximation Error estimation Fitting Estimator robustness Median Statistical estimation Approximation algorithm Statistical regression Statistical computation Least squares method Least square median Distribution function Quantile
ISSN:	0167-9473, 1872-7352
Online-Zugang:	Volltext
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