Fuzzy extensions of confidence intervals: Estimation for μ, σ2, and p

Cengiz Kahraman; Irem Otay; Başar Öztayşi

doi:10.1007/978-3-319-39014-7_9

Fuzzy extensions of confidence intervals: Estimation for μ, σ², and p

Cengiz Kahraman^*, Irem Otay, Başar Öztayşi

^*Corresponding author for this work

Department of Industrial Engineering

Istanbul Technical University

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

Even though classical point and interval estimations (PIE) are one of the most studied fields in statistics, there are a few numbers of studies covering fuzzy point and interval estimations. In this pursuit, this study focuses on analyzing the works on fuzzy PIE for the years between 1980 and 2015. In the chapter, the literature is reviewed through Scopus database and the review results are given by graphical illustrations. We also use the extensions of fuzzy sets such as interval-valued intuitionistic fuzzy sets (IVIFS) and hesitant fuzzy sets (HFS) to develop the confidence intervals based on these sets. The chapter also includes numerical examples to increase the understandability of the proposed approaches.

Original language	English
Pages (from-to)	129-154
Number of pages	26
Journal	Studies in Fuzziness and Soft Computing
Volume	343
DOIs	https://doi.org/10.1007/978-3-319-39014-7_9
Publication status	Published - 2016

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

Fuzzy
Hesitant fuzzy sets
Interval estimation
Interval-valued intuitionistic fuzzy sets
Point estimation

Access to Document

10.1007/978-3-319-39014-7_9

Cite this

@article{2163059a4a5d4e318ac5bbc2bcb1a001,

title = "Fuzzy extensions of confidence intervals: Estimation for μ, σ2, and p",

abstract = "Even though classical point and interval estimations (PIE) are one of the most studied fields in statistics, there are a few numbers of studies covering fuzzy point and interval estimations. In this pursuit, this study focuses on analyzing the works on fuzzy PIE for the years between 1980 and 2015. In the chapter, the literature is reviewed through Scopus database and the review results are given by graphical illustrations. We also use the extensions of fuzzy sets such as interval-valued intuitionistic fuzzy sets (IVIFS) and hesitant fuzzy sets (HFS) to develop the confidence intervals based on these sets. The chapter also includes numerical examples to increase the understandability of the proposed approaches.",

keywords = "Fuzzy, Hesitant fuzzy sets, Interval estimation, Interval-valued intuitionistic fuzzy sets, Point estimation",

author = "Cengiz Kahraman and Irem Otay and Ba{\c s}ar {\"O}ztay{\c s}i",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",

year = "2016",

doi = "10.1007/978-3-319-39014-7_9",

language = "English",

volume = "343",

pages = "129--154",

journal = "Studies in Fuzziness and Soft Computing",

issn = "1434-9922",

publisher = "Springer Verlag",

}

TY - JOUR

T1 - Fuzzy extensions of confidence intervals

T2 - Estimation for μ, σ2, and p

AU - Kahraman, Cengiz

AU - Otay, Irem

AU - Öztayşi, Başar

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - Even though classical point and interval estimations (PIE) are one of the most studied fields in statistics, there are a few numbers of studies covering fuzzy point and interval estimations. In this pursuit, this study focuses on analyzing the works on fuzzy PIE for the years between 1980 and 2015. In the chapter, the literature is reviewed through Scopus database and the review results are given by graphical illustrations. We also use the extensions of fuzzy sets such as interval-valued intuitionistic fuzzy sets (IVIFS) and hesitant fuzzy sets (HFS) to develop the confidence intervals based on these sets. The chapter also includes numerical examples to increase the understandability of the proposed approaches.

AB - Even though classical point and interval estimations (PIE) are one of the most studied fields in statistics, there are a few numbers of studies covering fuzzy point and interval estimations. In this pursuit, this study focuses on analyzing the works on fuzzy PIE for the years between 1980 and 2015. In the chapter, the literature is reviewed through Scopus database and the review results are given by graphical illustrations. We also use the extensions of fuzzy sets such as interval-valued intuitionistic fuzzy sets (IVIFS) and hesitant fuzzy sets (HFS) to develop the confidence intervals based on these sets. The chapter also includes numerical examples to increase the understandability of the proposed approaches.

KW - Fuzzy

KW - Hesitant fuzzy sets

KW - Interval estimation

KW - Interval-valued intuitionistic fuzzy sets

KW - Point estimation

UR - http://www.scopus.com/inward/record.url?scp=84978747022&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-39014-7_9

DO - 10.1007/978-3-319-39014-7_9

M3 - Article

AN - SCOPUS:84978747022

SN - 1434-9922

VL - 343

SP - 129

EP - 154

JO - Studies in Fuzziness and Soft Computing

JF - Studies in Fuzziness and Soft Computing

ER -