General Trimmed Estimation

Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1)

Research output: Working paperDiscussion paperOther research output

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Abstract

High breakdown-point regression estimators protect against large errors and data con- tamination. We generalize the concept of trimming used by many of these robust estima- tors, 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 B-mixing conditions and demonstrate its applicability in nonlinear regression and limited dependent variable models.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages41
Volume2007-65
Publication statusPublished - 2007

Publication series

NameCentER Discussion Paper
Volume2007-65

Fingerprint

Robust Estimators
Robust Estimation
Least Trimmed Squares
Estimator
Breakdown Point
Mixing Conditions
Nonlinear Regression Model
Trimming
Nonlinear Regression
Dependent
Regression Estimator
Asymptotic distribution
Likelihood
Generalise
Model
Demonstrate
Concepts
Standards

Keywords

  • asymptotic normality
  • regression
  • robust estimation
  • trimming

Cite this

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title = "General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1)",
abstract = "High breakdown-point regression estimators protect against large errors and data con- tamination. We generalize the concept of trimming used by many of these robust estima- tors, 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 B-mixing conditions and demonstrate its applicability in nonlinear regression and limited dependent variable models.",
keywords = "asymptotic normality, regression, robust estimation, trimming",
author = "P. Cizek",
note = "Subsequently published in Econometrics Theory, 2008 Pagination: 41",
year = "2007",
language = "English",
volume = "2007-65",
series = "CentER Discussion Paper",
publisher = "Operations research",
type = "WorkingPaper",
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}

General Trimmed Estimation : Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1). / Cizek, P.

Tilburg : Operations research, 2007. (CentER Discussion Paper; Vol. 2007-65).

Research output: Working paperDiscussion paperOther research output

TY - UNPB

T1 - General Trimmed Estimation

T2 - Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1)

AU - Cizek, P.

N1 - Subsequently published in Econometrics Theory, 2008 Pagination: 41

PY - 2007

Y1 - 2007

N2 - High breakdown-point regression estimators protect against large errors and data con- tamination. We generalize the concept of trimming used by many of these robust estima- tors, 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 B-mixing conditions and demonstrate its applicability in nonlinear regression and limited dependent variable models.

AB - High breakdown-point regression estimators protect against large errors and data con- tamination. We generalize the concept of trimming used by many of these robust estima- tors, 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 B-mixing conditions and demonstrate its applicability in nonlinear regression and limited dependent variable models.

KW - asymptotic normality

KW - regression

KW - robust estimation

KW - trimming

M3 - Discussion paper

VL - 2007-65

T3 - CentER Discussion Paper

BT - General Trimmed Estimation

PB - Operations research

CY - Tilburg

ER -