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Article

Title: Optimality of the least weighted squares estimator (English)
Author: Mašíček, Libor
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 40
Issue: 6
Year: 2004
Pages: [715]-734
Summary lang: English
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Category: math
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Summary: The present paper deals with least weighted squares estimator which is a robust estimator and it generalizes classical least trimmed squares. We will prove $\sqrt{n}$-consistency and asymptotic normality for any sequence of roots of normal equation for location model. The influence function for general case is calculated. Finally optimality of this estimator is discussed and formula for most B-robust and most V-robust weights is derived. (English)
Keyword: robust regression
Keyword: least trimmed squares
Keyword: least weighted squares
Keyword: influence function
Keyword: $\sqrt{n}$-consistency
Keyword: asymptotic normality
Keyword: B-robustness
Keyword: V-robustness
MSC: 62F10
MSC: 62F12
MSC: 62F35
MSC: 62J05
idZBL: Zbl 1245.62013
idMR: MR2120393
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Date available: 2009-09-24T20:05:36Z
Last updated: 2015-03-23
Stable URL: http://hdl.handle.net/10338.dmlcz/135629
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Reference: [1] Hampel F. R., Ronchetti E. M., Rousseeuw R. J., Stahel W. A.: Robust Statistics – The Approach Based on Influence Function.Wiley, New York 1986 MR 0829458
Reference: [2] Jurečková J.: Asymptotic Representation of M-estimators of Location.Math. Operationsforsch. Statist., Ser. Statistics 11 (1980), 1, 61–73 Zbl 0441.62037, MR 0606159
Reference: [3] Jurečková J., Sen P. K.: Robust Statistical Procedures.Wiley, New York 1996 Zbl 0862.62032, MR 1387346
Reference: [4] Mašíček L.: Konzistence odhadu LWS pro parametr polohy (Consistency of LWS estimator for location model).KPMS Preprint 25, Department of Probability and Mathematical Statistics, Faculty of Mathemetics and Physics, Charles University, Prague 2002
Reference: [5] Mašíček L.: Konzistence odhadu LWS pro parametr polohy (Consistency of LWS estimator for location model).In: ROBUST’2002 (J. Antoch, G. Dohnal, and J. Klaschka, eds.), JČMF 2002, pp. 240–246
Reference: [6] Rousseeuw P. J., Leroy A. M.: Robust Regression and Outlier Detection.J.Wiley, New York 1987 Zbl 0711.62030, MR 0914792
Reference: [7] Víšek J. Á.: Regression with high breakdown point.In: ROBUST’2000 (J. Antoch and G. Dohnal, eds.), JČMF 2001, pp. 324–356
Reference: [8] Víšek J. Á.: A new paradigm of point estimation.In: Data Analysis 2000 – Modern Statistical Methods – Modelling, Regression, Classification and Data Mining (K. Kupka, ed.), TRILOBYTE Software 2001, pp. 195–230
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