Testing fuzzy linear hypotheses in linear regression models

Autor:	Oke Gerke, Bernhard F. Arnold
Rok vydání:	2003
Předmět:	Statistics and Probability General linear model Fuzzy set Linear regression Statistics Applied mathematics Statistics Probability and Uncertainty Type (model theory) Linear hypothesis Fuzzy logic Mathematics Statistical hypothesis testing
Zdroj:	Metrika. 57:81-95
ISSN:	1435-926X 0026-1335
DOI:	10.1007/s001840200201
Popis:	In this paper statistical tests with fuzzily formulated hypotheses are discussed, i.e., hypotheses H0 and H1 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 H0 against the omnibus alternative H1: -H0. This is completely different from the classical case of testing crisp complementary hypotheses.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3d916d7e16e40cd1cd1b29e30af75e73 https://doi.org/10.1007/s001840200201 Zobrazit plný text záznamu Full text from SpringerLink