Testing fuzzy linear hypotheses in linear regression models

Bernhard Arnold and Oke Gerke

Metrika: International Journal for Theoretical and Applied Statistics, 2003, vol. 57, issue 1, 95 pages

Abstract: In this paper statistical tests with fuzzily formulated hypotheses are discussed, i.e., hypotheses H 0 and H 1 are fuzzy sets. The classical criteria of the errors of type I and type II are generalized, and this approach is applied to the linear hypothesis in the linear regression model. A sufficient condition to control both generalized criteria simultaneously is presented even in case of testing H 0 against the omnibus alternative H 1 : -H 0 . This is completely different from the classical case of testing crisp complementary hypotheses. Copyright Springer-Verlag 2003

Keywords: Fuzzy sets; hypotheses testing; linear regression (search for similar items in EconPapers)
Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s001840200201 (text/html)
Access to full text is restricted to subscribers.

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:metrik:v:57:y:2003:i:1:p:81-95

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s001840200201

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().