A distance-based statistical analysis of fuzzy number-valued data

Real-life data associated with experimental outcomes are not always real-valued. In particular, opinions, perceptions, ratings, etc., are often assumed to be vague in nature, especially when they come from human valuations. Fuzzy numbers have extensively been considered to provide us with a convenie...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of approximate reasoning 2014-10, Vol.55 (7), p.1487-1501
Hauptverfasser: Blanco-Fernández, A., Casals, M.R., Colubi, A., Corral, N., García-Bárzana, M., Gil, M.A., González-Rodríguez, G., López, M.T., Lubiano, M.A., Montenegro, M., Ramos-Guajardo, A.B., de la Rosa de Sáa, S., Sinova, B.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1501
container_issue 7
container_start_page 1487
container_title International journal of approximate reasoning
container_volume 55
creator Blanco-Fernández, A.
Casals, M.R.
Colubi, A.
Corral, N.
García-Bárzana, M.
Gil, M.A.
González-Rodríguez, G.
López, M.T.
Lubiano, M.A.
Montenegro, M.
Ramos-Guajardo, A.B.
de la Rosa de Sáa, S.
Sinova, B.
description Real-life data associated with experimental outcomes are not always real-valued. In particular, opinions, perceptions, ratings, etc., are often assumed to be vague in nature, especially when they come from human valuations. Fuzzy numbers have extensively been considered to provide us with a convenient tool to express these vague data. In analyzing fuzzy data from a statistical perspective one finds two key obstacles, namely, the nonlinearity associated with the usual arithmetic with fuzzy data and the lack of suitable models and limit results for the distribution of fuzzy-valued statistics. These obstacles can be frequently bypassed by using an appropriate metric between fuzzy data, the notion of random fuzzy set and a bootstrapped central limit theorem for general space-valued random elements. This paper aims to review these ideas and a methodology for the statistical analysis of fuzzy number data which has been developed along the last years. •Review on distances between fuzzy number-valued data for statistics.•Random fuzzy numbers and related summary measures.•Review on the statistical analysis of fuzzy number-valued data.
doi_str_mv 10.1016/j.ijar.2013.09.020
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1660038137</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0888613X13002120</els_id><sourcerecordid>1660038137</sourcerecordid><originalsourceid>FETCH-LOGICAL-c410t-51f78471e14afbe6979e6bb0736ca01a7e686b700ae0cfd68e6a980339fcc6913</originalsourceid><addsrcrecordid>eNp9kE1Lw0AQhhdRsFb_gKccvSTOdOPuBryU4hcUvCh4WyabCWxIm7qbFOqvd0s9exre4XkH5hHiFqFAQHXfFb6jUCwAZQFVAQs4EzM0WuallnguZmCMyRXKr0txFWMHAEqXZiaWy6zxcaSt47ymyE2Wwpg23lGf0Zb6Q_QxG9qsnX5-Dtl22tQc8j31U2IbGulaXLTUR775m3Px-fz0sXrN1-8vb6vlOnclwpg_YKtNqZGxpLZmVemKVV2DlsoRIGlWRtUagBhc2yjDiioDUlatc6pCORd3p7u7MHxPHEe78dFx39OWhylaVApAGpQ6oYsT6sIQY-DW7oLfUDhYBHv0ZTt79GWPvixUNvlKpcdTidMTe8_BRuc5eWl8YDfaZvD_1X8BmqVz0A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1660038137</pqid></control><display><type>article</type><title>A distance-based statistical analysis of fuzzy number-valued data</title><source>Elsevier ScienceDirect Journals Complete</source><source>EZB Electronic Journals Library</source><creator>Blanco-Fernández, A. ; Casals, M.R. ; Colubi, A. ; Corral, N. ; García-Bárzana, M. ; Gil, M.A. ; González-Rodríguez, G. ; López, M.T. ; Lubiano, M.A. ; Montenegro, M. ; Ramos-Guajardo, A.B. ; de la Rosa de Sáa, S. ; Sinova, B.</creator><creatorcontrib>Blanco-Fernández, A. ; Casals, M.R. ; Colubi, A. ; Corral, N. ; García-Bárzana, M. ; Gil, M.A. ; González-Rodríguez, G. ; López, M.T. ; Lubiano, M.A. ; Montenegro, M. ; Ramos-Guajardo, A.B. ; de la Rosa de Sáa, S. ; Sinova, B. ; SMIRE Research Group at the University of Oviedo</creatorcontrib><description>Real-life data associated with experimental outcomes are not always real-valued. In particular, opinions, perceptions, ratings, etc., are often assumed to be vague in nature, especially when they come from human valuations. Fuzzy numbers have extensively been considered to provide us with a convenient tool to express these vague data. In analyzing fuzzy data from a statistical perspective one finds two key obstacles, namely, the nonlinearity associated with the usual arithmetic with fuzzy data and the lack of suitable models and limit results for the distribution of fuzzy-valued statistics. These obstacles can be frequently bypassed by using an appropriate metric between fuzzy data, the notion of random fuzzy set and a bootstrapped central limit theorem for general space-valued random elements. This paper aims to review these ideas and a methodology for the statistical analysis of fuzzy number data which has been developed along the last years. •Review on distances between fuzzy number-valued data for statistics.•Random fuzzy numbers and related summary measures.•Review on the statistical analysis of fuzzy number-valued data.</description><identifier>ISSN: 0888-613X</identifier><identifier>EISSN: 1873-4731</identifier><identifier>DOI: 10.1016/j.ijar.2013.09.020</identifier><language>eng</language><publisher>Elsevier Inc</publisher><subject>Distance between fuzzy data ; Fuzzy ; Fuzzy data ; Fuzzy logic ; Fuzzy set theory ; Fuzzy systems ; Obstacles ; Random experiments ; Statistical analysis ; Statistics ; Theorems</subject><ispartof>International journal of approximate reasoning, 2014-10, Vol.55 (7), p.1487-1501</ispartof><rights>2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c410t-51f78471e14afbe6979e6bb0736ca01a7e686b700ae0cfd68e6a980339fcc6913</citedby><cites>FETCH-LOGICAL-c410t-51f78471e14afbe6979e6bb0736ca01a7e686b700ae0cfd68e6a980339fcc6913</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ijar.2013.09.020$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids></links><search><creatorcontrib>Blanco-Fernández, A.</creatorcontrib><creatorcontrib>Casals, M.R.</creatorcontrib><creatorcontrib>Colubi, A.</creatorcontrib><creatorcontrib>Corral, N.</creatorcontrib><creatorcontrib>García-Bárzana, M.</creatorcontrib><creatorcontrib>Gil, M.A.</creatorcontrib><creatorcontrib>González-Rodríguez, G.</creatorcontrib><creatorcontrib>López, M.T.</creatorcontrib><creatorcontrib>Lubiano, M.A.</creatorcontrib><creatorcontrib>Montenegro, M.</creatorcontrib><creatorcontrib>Ramos-Guajardo, A.B.</creatorcontrib><creatorcontrib>de la Rosa de Sáa, S.</creatorcontrib><creatorcontrib>Sinova, B.</creatorcontrib><creatorcontrib>SMIRE Research Group at the University of Oviedo</creatorcontrib><title>A distance-based statistical analysis of fuzzy number-valued data</title><title>International journal of approximate reasoning</title><description>Real-life data associated with experimental outcomes are not always real-valued. In particular, opinions, perceptions, ratings, etc., are often assumed to be vague in nature, especially when they come from human valuations. Fuzzy numbers have extensively been considered to provide us with a convenient tool to express these vague data. In analyzing fuzzy data from a statistical perspective one finds two key obstacles, namely, the nonlinearity associated with the usual arithmetic with fuzzy data and the lack of suitable models and limit results for the distribution of fuzzy-valued statistics. These obstacles can be frequently bypassed by using an appropriate metric between fuzzy data, the notion of random fuzzy set and a bootstrapped central limit theorem for general space-valued random elements. This paper aims to review these ideas and a methodology for the statistical analysis of fuzzy number data which has been developed along the last years. •Review on distances between fuzzy number-valued data for statistics.•Random fuzzy numbers and related summary measures.•Review on the statistical analysis of fuzzy number-valued data.</description><subject>Distance between fuzzy data</subject><subject>Fuzzy</subject><subject>Fuzzy data</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Fuzzy systems</subject><subject>Obstacles</subject><subject>Random experiments</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Theorems</subject><issn>0888-613X</issn><issn>1873-4731</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kE1Lw0AQhhdRsFb_gKccvSTOdOPuBryU4hcUvCh4WyabCWxIm7qbFOqvd0s9exre4XkH5hHiFqFAQHXfFb6jUCwAZQFVAQs4EzM0WuallnguZmCMyRXKr0txFWMHAEqXZiaWy6zxcaSt47ymyE2Wwpg23lGf0Zb6Q_QxG9qsnX5-Dtl22tQc8j31U2IbGulaXLTUR775m3Px-fz0sXrN1-8vb6vlOnclwpg_YKtNqZGxpLZmVemKVV2DlsoRIGlWRtUagBhc2yjDiioDUlatc6pCORd3p7u7MHxPHEe78dFx39OWhylaVApAGpQ6oYsT6sIQY-DW7oLfUDhYBHv0ZTt79GWPvixUNvlKpcdTidMTe8_BRuc5eWl8YDfaZvD_1X8BmqVz0A</recordid><startdate>20141001</startdate><enddate>20141001</enddate><creator>Blanco-Fernández, A.</creator><creator>Casals, M.R.</creator><creator>Colubi, A.</creator><creator>Corral, N.</creator><creator>García-Bárzana, M.</creator><creator>Gil, M.A.</creator><creator>González-Rodríguez, G.</creator><creator>López, M.T.</creator><creator>Lubiano, M.A.</creator><creator>Montenegro, M.</creator><creator>Ramos-Guajardo, A.B.</creator><creator>de la Rosa de Sáa, S.</creator><creator>Sinova, B.</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20141001</creationdate><title>A distance-based statistical analysis of fuzzy number-valued data</title><author>Blanco-Fernández, A. ; Casals, M.R. ; Colubi, A. ; Corral, N. ; García-Bárzana, M. ; Gil, M.A. ; González-Rodríguez, G. ; López, M.T. ; Lubiano, M.A. ; Montenegro, M. ; Ramos-Guajardo, A.B. ; de la Rosa de Sáa, S. ; Sinova, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c410t-51f78471e14afbe6979e6bb0736ca01a7e686b700ae0cfd68e6a980339fcc6913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Distance between fuzzy data</topic><topic>Fuzzy</topic><topic>Fuzzy data</topic><topic>Fuzzy logic</topic><topic>Fuzzy set theory</topic><topic>Fuzzy systems</topic><topic>Obstacles</topic><topic>Random experiments</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Theorems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Blanco-Fernández, A.</creatorcontrib><creatorcontrib>Casals, M.R.</creatorcontrib><creatorcontrib>Colubi, A.</creatorcontrib><creatorcontrib>Corral, N.</creatorcontrib><creatorcontrib>García-Bárzana, M.</creatorcontrib><creatorcontrib>Gil, M.A.</creatorcontrib><creatorcontrib>González-Rodríguez, G.</creatorcontrib><creatorcontrib>López, M.T.</creatorcontrib><creatorcontrib>Lubiano, M.A.</creatorcontrib><creatorcontrib>Montenegro, M.</creatorcontrib><creatorcontrib>Ramos-Guajardo, A.B.</creatorcontrib><creatorcontrib>de la Rosa de Sáa, S.</creatorcontrib><creatorcontrib>Sinova, B.</creatorcontrib><creatorcontrib>SMIRE Research Group at the University of Oviedo</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of approximate reasoning</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Blanco-Fernández, A.</au><au>Casals, M.R.</au><au>Colubi, A.</au><au>Corral, N.</au><au>García-Bárzana, M.</au><au>Gil, M.A.</au><au>González-Rodríguez, G.</au><au>López, M.T.</au><au>Lubiano, M.A.</au><au>Montenegro, M.</au><au>Ramos-Guajardo, A.B.</au><au>de la Rosa de Sáa, S.</au><au>Sinova, B.</au><aucorp>SMIRE Research Group at the University of Oviedo</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A distance-based statistical analysis of fuzzy number-valued data</atitle><jtitle>International journal of approximate reasoning</jtitle><date>2014-10-01</date><risdate>2014</risdate><volume>55</volume><issue>7</issue><spage>1487</spage><epage>1501</epage><pages>1487-1501</pages><issn>0888-613X</issn><eissn>1873-4731</eissn><abstract>Real-life data associated with experimental outcomes are not always real-valued. In particular, opinions, perceptions, ratings, etc., are often assumed to be vague in nature, especially when they come from human valuations. Fuzzy numbers have extensively been considered to provide us with a convenient tool to express these vague data. In analyzing fuzzy data from a statistical perspective one finds two key obstacles, namely, the nonlinearity associated with the usual arithmetic with fuzzy data and the lack of suitable models and limit results for the distribution of fuzzy-valued statistics. These obstacles can be frequently bypassed by using an appropriate metric between fuzzy data, the notion of random fuzzy set and a bootstrapped central limit theorem for general space-valued random elements. This paper aims to review these ideas and a methodology for the statistical analysis of fuzzy number data which has been developed along the last years. •Review on distances between fuzzy number-valued data for statistics.•Random fuzzy numbers and related summary measures.•Review on the statistical analysis of fuzzy number-valued data.</abstract><pub>Elsevier Inc</pub><doi>10.1016/j.ijar.2013.09.020</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0888-613X
ispartof International journal of approximate reasoning, 2014-10, Vol.55 (7), p.1487-1501
issn 0888-613X
1873-4731
language eng
recordid cdi_proquest_miscellaneous_1660038137
source Elsevier ScienceDirect Journals Complete; EZB Electronic Journals Library
subjects Distance between fuzzy data
Fuzzy
Fuzzy data
Fuzzy logic
Fuzzy set theory
Fuzzy systems
Obstacles
Random experiments
Statistical analysis
Statistics
Theorems
title A distance-based statistical analysis of fuzzy number-valued data
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T20%3A22%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20distance-based%20statistical%20analysis%20of%20fuzzy%20number-valued%20data&rft.jtitle=International%20journal%20of%20approximate%20reasoning&rft.au=Blanco-Fern%C3%A1ndez,%20A.&rft.aucorp=SMIRE%20Research%20Group%20at%20the%20University%20of%20Oviedo&rft.date=2014-10-01&rft.volume=55&rft.issue=7&rft.spage=1487&rft.epage=1501&rft.pages=1487-1501&rft.issn=0888-613X&rft.eissn=1873-4731&rft_id=info:doi/10.1016/j.ijar.2013.09.020&rft_dat=%3Cproquest_cross%3E1660038137%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1660038137&rft_id=info:pmid/&rft_els_id=S0888613X13002120&rfr_iscdi=true