A Confidence Set Analysis for Observed Samples: A Fuzzy Set Approach
AbstractConfidence sets are generally interpreted in terms of replications of an experiment. However, this interpretation is only valid before observing the sample. After observing the sample, any confidence sets have probability zero or one to contain the parameter value. In this paper, we provide a confidence set analysis for an observed sample based on fuzzy set theory by using the concept of membership functions. We show that the traditional ad hoc thresholds (the confidence and significance levels) can be attained from a general membership function. The applicability of the newly proposed theory is demonstrated by using well-known examples from the statistical literature and an application in the context of contingency tables. View Full-Text
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González, J.A.; Castro, L.M.; Lachos, V.H.; Patriota, A.G. A Confidence Set Analysis for Observed Samples: A Fuzzy Set Approach. Entropy 2016, 18, 211.
González JA, Castro LM, Lachos VH, Patriota AG. A Confidence Set Analysis for Observed Samples: A Fuzzy Set Approach. Entropy. 2016; 18(6):211.Chicago/Turabian Style
González, José A.; Castro, Luis M.; Lachos, Víctor H.; Patriota, Alexandre G. 2016. "A Confidence Set Analysis for Observed Samples: A Fuzzy Set Approach." Entropy 18, no. 6: 211.
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