Transformation and Weighting

David Ruppert
Additional contact information
David Ruppert: Cornell University

Chapter Chapter 2 in The Work of Raymond J. Carroll, 2014, pp 155-194 from Springer

Abstract: Abstract By the early 1980s, regression with homoscedastic errors was well understood, but methodology for handling heteroscedastic noise was just being developed. There were two general approaches. In the first, ​​​ studied by Carroll and Ruppert (1981 [TW-1], 1984 [TW-3]), the response is transformed to homoscedasticity. In the second, studied by Carroll and Ruppert (1982 [TW-2]) and Davidian and Carroll (1987 [TW-4]), one uses a variance function that ​​​ specifies the conditional variance of the response given the covariates. Transformation has the added feature that it can also reduce skewness of the errors, but transformation is useful only when the conditional variance is of a special form and, in particular, is a function of the conditional mean; this is a common occurrence, but there are many applications where it does not occur. Transformation and variance functions can be combined into a very general methodology as described ​​​ briefly below.

Keywords: Homoscedastic Errors; Conditional Variance; General Methodology; Common Occurrence; Life Sciences (search for similar items in EconPapers)
Date: 2014
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:978-3-319-05801-6_2

Ordering information: This item can be ordered from
http://www.springer.com/9783319058016

DOI: 10.1007/978-3-319-05801-6_2

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 ().