Robust Regression

Jonathon D. Brown
Additional contact information
Jonathon D. Brown: University of Washington, Department of Psychology

Chapter Chapter 7 in Advanced Statistics for the Behavioral Sciences, 2018, pp 219-252 from Springer

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-93549-2_7

Ordering information: This item can be ordered from
http://www.springer.com/9783319935492

DOI: 10.1007/978-3-319-93549-2_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().