A Monte Carlo Study of Two Robust Alternatives of Least Square Regression Estimation

Richard W. Hill, Paul W. Holland

We give some Monte Carlo results on the performance of two robust alternatives to least squares regression estimation - least absolute residuals and the one-step "sine" estimator. We show how to scale the residuals for the sine estimator to achieve constant efficiency at the Gaussian across various choices of X-matrix and give some results for the contaminated Gaussian distribution

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Year of publication:	September 1974
Authors:	Hill, Richard W.
Other Persons:	Holland, Paul W. (contributor)
Institutions:	National Bureau of Economic Research (contributor)
Publisher:	Cambridge, Mass : National Bureau of Economic Research
Subject:	Schätztheorie \| Estimation theory \| Kleinste-Quadrate-Methode \| Least squares method \| Monte-Carlo-Simulation \| Monte Carlo simulation \| Regressionsanalyse \| Regression analysis

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Extent:	1 Online-Ressource
Series:	NBER working paper series ; no. w0058
Type of publication:	Book / Working Paper
Language:	English
Notes:	Mode of access: World Wide Web System requirements: Adobe [Acrobat] Reader required for PDF files Hardcopy version available to institutional subscribers.
Other identifiers:	10.3386/w0058 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

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