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DC Field | Value | Language |
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dc.contributor.author | Golden, LL | - |
dc.contributor.author | Brockett, PL | - |
dc.contributor.author | Guillén, M | - |
dc.contributor.author | Manika, D | - |
dc.date.accessioned | 2020-05-05T11:56:25Z | - |
dc.date.available | 2020-05-05T11:56:25Z | - |
dc.date.issued | 2019-09-27 | - |
dc.identifier | ORCID iD: Danae Manika https://orcid.org/0000-0002-6331-1979 | - |
dc.identifier.citation | Golden, L.L. et al. (2020) 'aPRIDIT Unsupervised Classification with Asymmetric Valuation of Variable Discriminatory Worth', Multivariate Behavioral Research, 55 (5), pp. 685 - 703. doi: 10.1080/00273171.2019.1665979. | en_US |
dc.identifier.issn | 0027-3171 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/20768 | - |
dc.description.abstract | Copyright © 2019 The Author(s). Sometimes one needs to classify individuals into groups, but there is no available grouping information due to social desirability bias in reporting behavior like unethical or dishonest intentions or unlawful actions. Assessing hard-to-detect behaviors is useful; however it is methodologically difficult because people are unlikely to self-disclose bad actions. This paper presents an unsupervised classification methodology utilizing ordinal categorical predictor variables. It allows for classification, individual respondent ranking, and grouping without access to a dependent group indicator variable. The methodology also measures predictor variable worth (for determining target behavior group membership) at a predictor variable category-by-category level, so different variable response categories can contain different amounts of information about classification. It is asymmetric in that a “0” on a binary predictor does not have a similar impact toward signaling “membership in the target group” as a “1” has for signaling “membership in the non-target group.” The methodology is illustrated by identifying Spanish consumers filing fraudulent insurance claims. A second illustration classifies Portuguese high school student’s propensity to alcohol abuse. Results show the methodology is useful when it is difficult to get dependent variable information, and is useful for deciding which predictor variables and categorical response options are most important. | en_US |
dc.description.sponsorship | The work of Drs. Patrick Brockett and Linda Golden was supported by Grant SAB2003-0191 from the Spanish Ministry of Science, Grant 2004PIV1-00009 from Generalitat de Catalunya, Spain, and the Riskcenter, Department of Econometrics, University of Barcelona, Spain. The work of Dr. Montserrat Guillen was supported by Grant ECO2016-76203-C2- 2-P from ICREA Academia and the Spanish Ministry of Science. The work of Dr. Danae Manika was supported by a grant from the Center for Risk Management at the University of Texas at Austin, USA. | - |
dc.format.extent | 685 - 703 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.rights | Copyright © 2019 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (https://creativecommons.org/licenses/bync-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. | - |
dc.rights.uri | https://creativecommons.org/licenses/bync-nd/4.0/ | - |
dc.subject | detecting hidden behavior, | en_US |
dc.subject | classification into non-self-disclosed behavior groups, | en_US |
dc.subject | unsupervised learning, | en_US |
dc.subject | asymmetric measures | en_US |
dc.subject | non-parametric classification, | en_US |
dc.title | aPRIDIT Unsupervised Classification with Asymmetric Valuation of Variable Discriminatory Worth | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1080/00273171.2019.1665979 | - |
dc.relation.isPartOf | Multivariate Behavioral Research | - |
pubs.issue | 5 | - |
pubs.publication-status | Published | - |
pubs.volume | 55 | - |
dc.identifier.eissn | 1532-7906 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Brunel Business School Research Papers |
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FullText.pdf | Copyright © 2019 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (https://creativecommons.org/licenses/bync-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. | 1.73 MB | Adobe PDF | View/Open |
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