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題名 Estimating retail demand with Poisson mixtures and out-of-sample likelihood
作者 莊皓鈞
Chuang,Howard Hao-Chun ;Oliva, Rogelio
貢獻者 資管系
關鍵詞 retail demand; Poisson mixtures; dispersion; maximum likelihood; model selection
日期 2013.07
上傳時間 19-Dec-2013 11:11:15 (UTC+8)
摘要 Estimation of retail demand is critical to decisions about procuring, shipping, and shelving. The idea of Poisson demand process is central to retail inventory management and numerous studies suggest that negative binomial (NB) distribution characterize retail demand well. In this study, we reassess the adequacy of estimating retail demand with the NB distribution. We propose two Poisson mixtures—the Poisson–Tweedie family (PTF) and the Conway–Maxwell–Poisson distribution—as generic alternatives to the NB distribution. On the basis of the principle of likelihood and information theory, we adopt out-of-sample likelihood as a metric for model selection. We test the procedure on consumer demand for 580 stock-keeping unit store sales datasets. Overall the PTF and the Conway–Maxwell–Poisson distribution outperform the NB distribution for 70% of the tested samples. As a general case of the NB model, the PTF has particularly strong performance for datasets with relatively small means and high dispersion. Our finding carries useful implications for researchers and practitioners who seek for flexible alternatives to the oft-used NB distribution in characterizing retail demand.
關聯 Applied Stochastic Models in Business and Industry, 30(4), 455-463
資料類型 article
DOI http://dx.doi.org/10.1002/asmb.1986
dc.contributor 資管系en_US
dc.creator (作者) 莊皓鈞zh_TW
dc.creator (作者) Chuang,Howard Hao-Chun ;Oliva, Rogelioen_US
dc.date (日期) 2013.07en_US
dc.date.accessioned 19-Dec-2013 11:11:15 (UTC+8)-
dc.date.available 19-Dec-2013 11:11:15 (UTC+8)-
dc.date.issued (上傳時間) 19-Dec-2013 11:11:15 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/62694-
dc.description.abstract (摘要) Estimation of retail demand is critical to decisions about procuring, shipping, and shelving. The idea of Poisson demand process is central to retail inventory management and numerous studies suggest that negative binomial (NB) distribution characterize retail demand well. In this study, we reassess the adequacy of estimating retail demand with the NB distribution. We propose two Poisson mixtures—the Poisson–Tweedie family (PTF) and the Conway–Maxwell–Poisson distribution—as generic alternatives to the NB distribution. On the basis of the principle of likelihood and information theory, we adopt out-of-sample likelihood as a metric for model selection. We test the procedure on consumer demand for 580 stock-keeping unit store sales datasets. Overall the PTF and the Conway–Maxwell–Poisson distribution outperform the NB distribution for 70% of the tested samples. As a general case of the NB model, the PTF has particularly strong performance for datasets with relatively small means and high dispersion. Our finding carries useful implications for researchers and practitioners who seek for flexible alternatives to the oft-used NB distribution in characterizing retail demand.en_US
dc.format.extent 463884 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Applied Stochastic Models in Business and Industry, 30(4), 455-463en_US
dc.subject (關鍵詞) retail demand; Poisson mixtures; dispersion; maximum likelihood; model selectionen_US
dc.title (題名) Estimating retail demand with Poisson mixtures and out-of-sample likelihooden_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1002/asmb.1986en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1002/asmb.1986en_US