When is the inverse regression estimator MSE-superior to the standard regression estimator in multivariate controlled calibration situations?

Rolf Sundberg

Statistics & Probability Letters, 1985, vol. 3, issue 2, 75-79

Abstract: We assume as model a standard multivariate regression of y on x, fitted to a controlled calibration sample and used to estimate unknown x's from observed y-values. The standard weighted least squares estimator ('classical', regress y on x and 'solve' for x) and the biased inverse regression estimator (regress x on y) are compared with respect to mean squared error. The regions are derived where the inverse regression estimator yields the smaller MSE. For any particular component of x this region is likely to contain 'most' future values in usual practice. For simultaneous estimation this needs not be true, however.

Keywords: mean; squared; error; multivariate; regression (search for similar items in EconPapers)
Date: 1985
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(85)90028-8
Full text for ScienceDirect subscribers only

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:eee:stapro:v:3:y:1985:i:2:p:75-79

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().