Robust Line Estimation With Errors in Both Variables /
Brown, Michael L.
Robust Line Estimation With Errors in Both Variables / Michael L. Brown. - Cambridge, Mass. National Bureau of Economic Research 1975. - 1 online resource: illustrations (black and white); - NBER working paper series no. w0083 . - Working Paper Series (National Bureau of Economic Research) no. w0083. .
May 1975.
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.
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Mode of access: World Wide Web.
Robust Line Estimation With Errors in Both Variables / Michael L. Brown. - Cambridge, Mass. National Bureau of Economic Research 1975. - 1 online resource: illustrations (black and white); - NBER working paper series no. w0083 . - Working Paper Series (National Bureau of Economic Research) no. w0083. .
May 1975.
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.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.