A robust regression technique using compound estimation

James R. Simpson, Douglas Montgomery

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Least squares fitting of regression models is a widely used technique. The presence of outliers in the data can have an adverse effect on the method of least squares, resulting in a model that does not adequately fit to the bulk of the data. For this situation, robust regression techniques have been proposed as an improvement to the method of least squares. We propose a robust regression procedure that performs well relative to the current robust methods against a variety of dataset types. Evaluations are performed using datasets without outliers (testing efficiency), with a large percentage of outliers (testing breakdown), and with high leverage outliers (testing bounded influence). The datasets are based on 2-level factorial designs that include axial points to evaluate leverage effects. A Monte Carlo simulation approach is used to evaluate the estimating capability of the proposed procedure relative to several competing methods. We also provide an application to estimating costs for government satellites.

Original languageEnglish (US)
Pages (from-to)125-139
Number of pages15
JournalNaval Research Logistics
Volume45
Issue number2
StatePublished - Mar 1998

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Robust Regression
Outlier
Testing
Cost estimating
Least Squares
Bounded Influence
Leverage Effect
Least Square Fitting
Factorial Design
Evaluate
Robust Methods
Leverage
Satellites
Breakdown
Percentage
Regression Model
Monte Carlo Simulation
Robust regression
Outliers
Evaluation

ASJC Scopus subject areas

  • Management Science and Operations Research

Cite this

A robust regression technique using compound estimation. / Simpson, James R.; Montgomery, Douglas.

In: Naval Research Logistics, Vol. 45, No. 2, 03.1998, p. 125-139.

Research output: Contribution to journalArticle

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