Pearson chi-square estimator and test for log-linear models with expected frequencies subject to linear constraints

Douglas G. Bonett

Statistics & Probability Letters, 1989, vol. 8, issue 2, 175-177

Abstract: The minimum Pearson chi-square estimator and test are defined for multinomial log-linear models with expected frequencies subject to linear constraints. The minimum Pearson chi-square estimate defined here yields predicted cell frequency estimates that, unlike the maximum likelihood estimates, minimize the popular Pearson chi-square test statistic.

Keywords: minimum; Pearson; chi-square; nonlinear; optimization; penalty; function (search for similar items in EconPapers)
Date: 1989
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(89)90013-8
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:8:y:1989:i:2:p:175-177

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 ().