A computational examination of orthogonal distance regression

Ordinary least squares (OLS) is one of the most commonly used criteria for fitting data to models and for estimating parameters. Orthogonal distance regression (ODR) extends least squares data fitting to problems with independent variables that are not known exactly. In this paper, we present the re...

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Bibliographic Details
Published inJournal of econometrics Vol. 38; no. 1; pp. 169 - 201
Main Authors Boggs, Paul T., Spiegelman, Clifford H., Donaldson, Janet R., Schnabel, Robert B.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.05.1988
Elsevier
North-Holland Pub. Co
Elsevier Sequoia S.A
SeriesJournal of Econometrics
Subjects
Algorithms
Discriminant analysis
Econometrics
Exact sciences and technology
Mathematics
Monte Carlo simulation
Multivariate analysis
Probability and statistics
Sciences and techniques of general use
Statistics
Regression
Statistical estimation
Fitting
Least squares method
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Summary:Ordinary least squares (OLS) is one of the most commonly used criteria for fitting data to models and for estimating parameters. Orthogonal distance regression (ODR) extends least squares data fitting to problems with independent variables that are not known exactly. In this paper, we present the results of an empirical study designed to compare OLS to ODR for fitting both linear and non-linear models when there are errors in the independent variables. The results indicate that, for the data and performance criteria considered, ODR never performs appreciably worse than OLS and often performs considerably better.
ISSN:0304-4076
1872-6895
DOI:10.1016/0304-4076(88)90032-2