Neyman-Pearson Lemma for fuzzy hypotheses testing

Metrika

Volumn 49, Issue 1, 1999, Pages 3-17

  Taheri, S M ^a   Behboodian, Javad ^a  

^a SHIRAZ UNIVERSITY   (Iran)

Author keywords

Fuzzy hypothesis; Neyman Pearson Lemma; Probability of type I and type II errors

Indexed keywords

EID: 0033481189     PISSN: 00261335     EISSN: None     Source Type: Journal    
DOI: 10.1007/s001840050021     Document Type: Article

References (12)

1. Arnold BF (1996) An approach to fuzzy hypothesis testing. Metrika 44:119-126
2. Billingsley P (1986) Probability and measure. John Wiley, New York
3. Buckley JJ (1985) Fuzzy decision making with data: applications to statistics. Fuzzy Sets and Systems 16:139-147
4. Casals MR, Gil MA, Gil P (1986) On the use of Zadeh's probabilistic definition for testing statistical hypotheses form fuzzy information. Fuzzy Sets and Systems 20:175-190
5. Casals MR (1993) Bayesian testing of fuzzy parametric hypotheses from fuzzy information. RAIRO, OR 27, 2:189-199
6. Casella G, Berger RL(1990) Statistical inference. Brooks/Cole Publishing, California
7. Kruse R, Meyer KD (1987) Statistics with vague data. Reidal Publ. comp., Dordrech, Netherlands
8. Kwakernaak H (1978) Fuzzy random variables: definition and theorem. Inform. Sci. 15:1-29
9. Puri ML, Ralescu DA (1986) Fuzzy random variables. J. Math. Anal. Appl. 114:409-422
10. Saade JJ, Schwarzlander H (1990) Fuzzy hypothesis testing with hybrid data. Fuzzy Sets and Systems 35:197-212
11. Tanaka H, Okuda T, Asai K (1979) Fuzzy information and decision in statistical model. In: Advances in fuzzy set theory and applications, North-Holland, Amsterdam pp. 303-320
12. Zadeh LA (1965) Fuzzy sets. Information and Control 8:338-353