On the efficient computation of robust regression estimators

The problem of providing efficient and reliable robust regression algorithms is considered. The impact of global optimization methods, such as stopping conditions and clustering techniques, in the calculation of robust regression estimators is investigated. The use of stopping conditions permits us...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computational statistics & data analysis Ročník 54; číslo 12; s. 3044 - 3056
Hlavný autor: Flores, Salvador
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2010
Elsevier
Edícia:Computational Statistics & Data Analysis
Predmet:
Algorithms
Clustering
Computational efficiency
Estimators
Global optimization
Mathematical analysis
Optimization
Regression
Robust regression
Robust regression Clustering Global optimization Stopping conditions
Statistics
Stopping conditions
Global optimization
Stopping conditions
Robust regression
Clustering
ISSN:0167-9473, 1872-7352
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The problem of providing efficient and reliable robust regression algorithms is considered. The impact of global optimization methods, such as stopping conditions and clustering techniques, in the calculation of robust regression estimators is investigated. The use of stopping conditions permits us to devise new algorithms that perform as well as the existing algorithms in less time and with adaptive algorithm parameters. Clustering global optimization is shown to be a general framework encompassing many of the existing algorithms.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2010.03.020