A Computational Examination of Orthogonal Distance Regression.

@inproceedings{Boggs1988ACE,
  title={A Computational Examination of Orthogonal Distance Regression.},
  author={Paul T. Boggs and Clifford H. Spiegelman and Janet R. Donaldson and Robert B. Schnabel},
  year={1988}
}
Abstract 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… CONTINUE READING

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Orthogonal distance regression

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