Asymptotics for one-step m-estimators in regression with application to combining efficiency and high breakdown point

1987 ◽  
Vol 16 (8) ◽  
pp. 2187-2199 ◽  
Author(s):  
Jana Jurečková ◽  
Stephen Portnoy
Keyword(s):  
Breakdown Point ◽  
High Breakdown Point ◽  
Combining Efficiency ◽  
One Step

About Regression Estimators with High Breakdown Point

Statistics ◽  
1998 ◽  
Vol 32 (2) ◽  
pp. 111-129 ◽  
Author(s):  
D. L. Vandev ◽  
N. M. Neykov*
Keyword(s):  
Breakdown Point ◽  
High Breakdown Point ◽  
Regression Estimators

scholarly journals GENERAL TRIMMED ESTIMATION: ROBUST APPROACH TO NONLINEAR AND LIMITED DEPENDENT VARIABLE MODELS

2008 ◽  
Vol 24 (6) ◽  
pp. 1500-1529 ◽  
Author(s):  
Pavel Čížek
Keyword(s):  
Regression Models ◽  
Breakdown Point ◽  
Robust Estimators ◽  
Linear Regression Models ◽  
Mixing Conditions ◽  
Least Trimmed Squares ◽  
Limited Dependent Variable ◽  
Data Contamination ◽  
High Breakdown Point ◽  
Regression Estimators

High-breakdown-point regression estimators protect against large errors and data contamination. We generalize the concept of trimming used by many of these robust estimators, such as the least trimmed squares and maximum trimmed likelihood, and propose a general trimmed estimator, which renders robust estimators applicable far beyond the standard (non)linear regression models. We derive here the consistency and asymptotic distribution of the proposed general trimmed estimator under mild β-mixing conditions and demonstrate its applicability in nonlinear regression and limited dependent variable models.

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