The Use and Misuse

نویسندگان

  • R. J. Carroll
  • David Ruppert
چکیده

Orthogonal regression is one of the standard linear regression methods to correct for the eeects of measurement error in predictors. We argue that orthogonal regression is often misused in errors-in-variables linear regression, because of a failure to account for equation errors. The typical result is to overcorrect for measurement error, i.e., overestimate the slope, because equation error is ignored. The use of orthogonal regression must include a careful assessment of equation error, and not merely the usual (often informal) estimation of the ratio of measurement error variances. There are rarer instances, e.g., an example from geology discussed here, where the use of orthogonal regression without proper attention to modeling may lead to either overcorrection or undercorrection, depending on the relative sizes of the variances involved. Thus, our main point, which does not seem to be widely appreciated, is that orthogonal regression requires rather careful modeling of error.

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تاریخ انتشار 1994