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dc.contributor.author
Tarek Abdallah  
dc.contributor.author
Vulcano, Gustavo  
dc.date.available
2023-08-09T13:08:47Z  
dc.date.issued
2021-09  
dc.identifier.citation
Tarek Abdallah; Vulcano, Gustavo; Demand estimation under the multinomial logit model from sales transaction data; Informs; M&som-manufacturing & Service Operations Management; 23; 5; 9-2021; 1196-1216  
dc.identifier.issn
1523-4614  
dc.identifier.uri
http://hdl.handle.net/11336/207564  
dc.description.abstract
Problem definition: A major task in retail operations is to optimize the assortments exhibited to consumers. To this end, retailers need to understand customers' preferences for different products. Academic/practical relevance: This is particularly challenging when only sales and product-availability data are recorded, and not all products are displayed in all periods. Similarly, in revenue management contexts, firms (airlines, hotels, etc.) need to understand customers' preferences for different options in order to optimize the menu of products to offer. Methodology: In this paper, we study the estimation of preferences under a multinomial logit model of demand when customers arrive over time in accordance with a nonhomogeneous Poisson process. This model has recently caught important attention in both academic and industrial practices. We formulate the problem as a maximum-likelihood estimation problem, which turns out to be nonconvex. Results: Our contribution is twofold: From a theoretical perspective, we characterize conditions under which the maximum-likelihood estimates are unique and the model is identifiable. From a practical perspective, we propose a minorization-maximization (MM) algorithm to ease the optimization of the likelihood function. Through an extensive numerical study, we show that our algorithm leads to better estimates in a noticeably short computational time compared with state-of-the-art benchmarks. Managerial implications: The theoretical results provide a solid foundation for the use of the model in terms of the quality of the derived estimates. At the same time, the fast MM algorithm allows the implementation of the model and the estimation procedure at large scale, compatible with real industrial applications.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Informs  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
CHOICE BEHAVIOR  
dc.subject
DEMAND UNCENSORING  
dc.subject
EXPECTATION-MAXIMIZATION (EM) ALGORITHM  
dc.subject
MAXIMUM-LIKELIHOOD (ML) ESTIMATION  
dc.subject
MINORIZATION-MAXIMIZATION (MM) ALGORITHM  
dc.subject
RETAIL OPERATIONS  
dc.subject
REVENUE MANAGEMENT  
dc.subject.classification
Matemática Aplicada  
dc.subject.classification
Matemáticas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.subject.classification
Economía, Econometría  
dc.subject.classification
Economía y Negocios  
dc.subject.classification
CIENCIAS SOCIALES  
dc.title
Demand estimation under the multinomial logit model from sales transaction data  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-08-08T13:05:14Z  
dc.identifier.eissn
1526-5498  
dc.journal.volume
23  
dc.journal.number
5  
dc.journal.pagination
1196-1216  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Catonsville  
dc.description.fil
Fil: Tarek Abdallah. Kellogg School Of Management; Estados Unidos  
dc.description.fil
Fil: Vulcano, Gustavo. Universidad Torcuato Di Tella. Escuela de Negocios; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.journal.title
M&som-manufacturing & Service Operations Management  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1287/msom.2020.0878  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://pubsonline.informs.org/doi/10.1287/msom.2020.0878