The least trimmed squares. Part II: -consistency
Kybernetika (2006)
- Volume: 42, Issue: 2, page 181-202
- ISSN: 0023-5954
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topVíšek, Jan Ámos. "The least trimmed squares. Part II: $\sqrt{n}$-consistency." Kybernetika 42.2 (2006): 181-202. <http://eudml.org/doc/33800>.
@article{Víšek2006,
abstract = {$\sqrt\{n\}$-consistency of the least trimmed squares estimator is proved under general conditions. The proof is based on deriving the asymptotic linearity of normal equations.},
author = {Víšek, Jan Ámos},
journal = {Kybernetika},
keywords = {robust regression; the least trimmed squares; $\sqrt\{n\}$-consistency; asymptotic normality; robust regression; asymptotic normality},
language = {eng},
number = {2},
pages = {181-202},
publisher = {Institute of Information Theory and Automation AS CR},
title = {The least trimmed squares. Part II: $\sqrt\{n\}$-consistency},
url = {http://eudml.org/doc/33800},
volume = {42},
year = {2006},
}
TY - JOUR
AU - Víšek, Jan Ámos
TI - The least trimmed squares. Part II: $\sqrt{n}$-consistency
JO - Kybernetika
PY - 2006
PB - Institute of Information Theory and Automation AS CR
VL - 42
IS - 2
SP - 181
EP - 202
AB - $\sqrt{n}$-consistency of the least trimmed squares estimator is proved under general conditions. The proof is based on deriving the asymptotic linearity of normal equations.
LA - eng
KW - robust regression; the least trimmed squares; $\sqrt{n}$-consistency; asymptotic normality; robust regression; asymptotic normality
UR - http://eudml.org/doc/33800
ER -
References
top- Čížek P., Analýza citlivosti -krokových -odhadů (Sensitivity analysis of -step -estimators, in Czech), Diploma Thesis, Czech Technical University, Prague 1996
- Hewitt E., Stromberg K., Real and Abstract Analysis, Springer–Verlag, Berlin 1965 Zbl0307.28001MR0367121
- Víšek J. Á., 10.1007/BF00050849, Ann. Inst. Statist. Math. 48 (1996), 469–495 (1996) MR1424776DOI10.1007/BF00050849
- Víšek J. Á., The least trimmed squares, Part I. Consistency. Kybernetika 42 (2006), 1–36 MR2208518
- Víšek J. Á., Kolmogorov–Smirnov statistics in linear regression, In: Proc. ROBUST 2006, submitted
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