http://worldcat.org/entity/work/id/1020838073

Robust line estimation with errors in both variables

"The estimator holding the central place in the theory of the multivariate "errors-in-the-variables" (EV) model results from performing orthogonal recession on variables rescaled according to the covariance matrix of the errors [7]. Our first principal finding, via Monte Carlo on the univariate model, essentially relegates this estimator to use only in large samples on very well-behaved data, i.e., with no trace of outlier contamination. A modification, requiring a robust preliminary slope, is proposed that essentially sets out the generalization to EV of the w-estimator in regression. It is demonstrated that the modification is robust to outlier contamination even in small samples, given a sufficiently good preliminary estimator. A candidate for a preliminary slope estimator based on the data is proposed arid its performance under simulation examined. Least-absolute residuals estimation in EV is cited as an alternative candidate"--NBER website.

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