Probabilistic Preferences Composition in the Classification of Apparel Retail Stores

Rodrigo Otávio de Araújo Ribeiro, Lidia Angulo Meza and Annibal Parracho Sant'Anna
Additional contact information
Rodrigo Otávio de Araújo Ribeiro: IBOPE-DTM, Rio de Janeiro, Brazil
Lidia Angulo Meza: Universidade Federal Fluminense, Niteroi, Brazil
Annibal Parracho Sant'Anna: Universidade Federal Fluminense, Niteroi, Brazil

International Journal of Business Analytics (IJBAN), 2015, vol. 2, issue 4, 64-78

Abstract: This paper employs the probabilistic composition of preferences to classify stores by their operational efficiency. Probabilistic composition of preferences is a multicriteria analysis methodology based on the transformation of assessments by multiple attributes into probabilities of choice. The numerical initial measurements provide estimates for location parameters of probability distributions that are compared to measure the preferences. The probabilities of choice according to each attribute separately are aggregated according to probabilistic composition rules. A classification of two sets of stores into five classes is performed.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJBAN.2015100104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:2:y:2015:i:4:p:64-78

Access Statistics for this article

International Journal of Business Analytics (IJBAN) is currently edited by John Wang

More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().