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Asymptotics of least trimmed squares regression

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Author Info
Cizek, P. (Tilburg University, Center for Economic Research)

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Abstract

High breakdown-point regression estimators protect against large errors both in explanatory and dependent variables. The least trimmed squares (LTS) estimator is one of frequently used, easily understandable, and thoroughly studied (from the robustness point of view) high breakdown-point estimators. In spite of its increasing popularity and number of applications, there are only conjectures and hints about its asymptotic behavior in regression after two decades of its existence. We derive here all important asymptotic properties of LTS, including the asymptotic normality and variance, under mild /-mixing conditions.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 72.

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Date of creation: 2004
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Handle: RePEc:dgr:kubcen:200472

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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  1. White, Halbert, 1980. "Nonlinear Regression on Cross-Section Data," Econometrica, Econometric Society, vol. 48(3), pages 721-46, April. [Downloadable!] (restricted)
  2. Zinde-Walsh, Victoria, 2002. "Asymptotic Theory For Some High Breakdown Point Estimators," Econometric Theory, Cambridge University Press, vol. 18(05), pages 1172-1196, October. [Downloadable!]
  3. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, vol. 71(1), pages 1-8, April. [Downloadable!] (restricted)
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This page was last updated on 2009-11-25.

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