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ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2017,12,19]]},"abstract":"<jats:p>\n            In this paper, the question of interest is estimating\n            <jats:italic>true<\/jats:italic>\n            demand of a product at a given store location and time period in the retail environment based on a\n            <jats:italic>single<\/jats:italic>\n            noisy and potentially censored observation. To address this question, we introduce a %non-parametric framework to make inference from multiple time series. Somewhat surprisingly, we establish that the algorithm introduced for the purpose of \"matrix completion\" can be used to solve the relevant inference problem. Specifically, using the Universal Singular Value Thresholding (USVT) algorithm [7], we show that our estimator is consistent: the average mean squared error of the estimated average demand with respect to the true average demand goes to\n            <jats:italic>0<\/jats:italic>\n            as the number of store locations and time intervals increase to $\\infty$. We establish naturally appealing properties of the resulting estimator both analytically as well as through a sequence of instructive simulations. Using a real dataset in retail (Walmart), we argue for the practical relevance of our approach.\n          <\/jats:p>","DOI":"10.1145\/3154489","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T18:28:08Z","timestamp":1521829688000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Censored Demand Estimation in Retail"],"prefix":"10.1145","volume":"1","author":[{"given":"Muhammad J.","family":"Amjad","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devavrat","family":"Shah","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,12,19]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2014. 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The data-driven (s S) policy: The data-driven (s S) policy: The data driven (s S) policy: why you can have confidence in censored demand data. Available at SSRN: https:\/\/ssrn.com\/abstract=2654014 (2015).  Gah-Yi Ban. 2015. The data-driven (s S) policy: The data-driven (s S) policy: The data driven (s S) policy: why you can have confidence in censored demand data. Available at SSRN: https:\/\/ssrn.com\/abstract=2654014 (2015)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.48.3.436.12437"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1272"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Li Chen and Adam J. Mersereau. 2015. Analytics for Operational Visibility in the Retail Store: The Cases of Censored Demand and Inventory Record Inaccuracy. Springer US Boston MA 79--112.  Li Chen and Adam J. Mersereau. 2015. 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