计算机科学
骨料(复合)
排名(信息检索)
集合(抽象数据类型)
班级(哲学)
交易数据
数据集
计量经济学
数据挖掘
数学优化
数据库事务
机器学习
人工智能
数学
材料科学
复合材料
程序设计语言
作者
Srikanth Jagabathula,Dmitry Mitrofanov,Gustavo Vulcano
出处
期刊:Operations Research
[Institute for Operations Research and the Management Sciences]
日期:2023-09-04
卷期号:72 (1): 19-42
被引量:14
标识
DOI:10.1287/opre.2022.0006
摘要
In “Demand Estimation Under Uncertain Consideration Sets,” Jagabathula, Mitrofanov, and Vulcano investigate statistical properties of the consider-then-choose (CTC) models, which gained recent attention in the operations literature as an alternative to the classical random utility (RUM) models. The general class of CTC models is defined by a general joint distribution over ranking lists and consideration sets. Starting from the important result that the CTC and RUM classes are equivalent in terms of explanatory power, the authors characterize conditions under which CTC models become identified. Then, they propose expectation-maximization (EM) methods to solve the related estimation problem for different subclasses of CTC models, building from the provably convergent outer-approximation algorithm. Finally, subclasses of CTC models are tested on a synthetic data set and on two real data sets: one from a grocery chain and one from a peer-to-peer (P2P) car sharing platform. The results are consistent in assessing that CTC models tend to dominate RUM models with respect to prediction accuracy when the training data are noisy (i.e., transaction records do not necessarily reflect the physical inventory records) and when there is significant asymmetry between the training data set and the testing data set. These conditions are naturally verified in P2P sharing platforms and in retailers working on long-term forecasts (e.g., semester long) or geographical aggregate forecasts (e.g., forecasts at the distribution center level).
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