Please use this identifier to cite or link to this item:
Type: Artigo
Title: A Confidence Set Analysis For Observed Samples: A Fuzzy Set Approach
Author: Alejandro Gonzalez
Jose; Mauricio Castro
Luis; Lachos
Victor Hugo; Patriota
Alexandre Galvao
Abstract: Confidence 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.
Subject: Confidence Sets
Fuzzy Sets
Membership Function
Possibility Theory
Editor: MDPI AG
Citation: Entropy. Mdpi Ag, v. 18, p. , 2016.
Rights: aberto
Identifier DOI: 10.3390/e18060211
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
000378843200015.pdf884.85 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.