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Robust Regression

In: Advanced Statistics for the Behavioral Sciences

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  • Jonathon D. Brown

    (University of Washington, Department of Psychology)

Abstract

In Chap. 6 we learned how to detect and manage violations of the Gauss-Markov theorem. In this chapter, we consider a related problem—how to accommodate errors that are not normally distributed. Normally distributed errors are not demanded by the Gauss-Markov theorem, but the errors need to be at least approximately normal if we wish to use the normal distribution to test hypotheses about the regression coefficients or construct confidence intervals around them. Fortunately, the central limit theorem tells us that if our criterion is normally distributed, the errors will also be normally distributed with large samples. Normality is less certain with small samples, however, so it is important to examine the residuals to be sure that they are, at least, approximately normal and to take appropriate action if they are found not to be so.

Suggested Citation

  • Jonathon D. Brown, 2018. "Robust Regression," Springer Books, in: Advanced Statistics for the Behavioral Sciences, chapter 0, pages 219-252, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-93549-2_7
    DOI: 10.1007/978-3-319-93549-2_7
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