NDLI: Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing

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Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing

Content Provider	Scilit
Author	Chen, Xi Wang, Yining
Copyright Year	2020
Description	Journal: SSRN Electronic Journal Data-driven sequential decision has found a wide range of applications in modern operations management, such as dynamic pricing, inventory control, and assortment optimization. Most existing research on data-driven sequential decision focuses on designing an online policy to maximize the revenue. However, the research on uncertainty quantification on the underlying true model function (e.g., demand function), a critical problem for practitioners, has not been well explored. In this paper, using the problem of demand function prediction in dynamic pricing as the motivating example, we study the problem of constructing accurate confidence intervals for the demand function. The main challenge is that sequentially collected data leads to significant distributional bias in the maximum likelihood estimator or the empirical risk minimization estimate, making classical statistics approaches such as the Wald's test no longer valid. We address this challenge by developing a debiased approach and provide the asymptotic normality guarantee of the debiased estimator. Based this the debiased estimator, we provide both point-wise and uniform confidence intervals of the demand function.
Related Links	http://arxiv.org/pdf/2003.07017 https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3554836
ISSN	10914358
e-ISSN	15565068
DOI	10.2139/ssrn.3554836
Journal	SSRN Electronic Journal
Language	English
Publisher	Elsevier BV
Publisher Date	2020-03-16
Access Restriction	Open
Subject Keyword	Journal: SSRN Electronic Journal Operations Research and Management Science Adaptive Data Asymptotic Normality Confidence Interval Dynamic Pricing Data-driven Sequential Decision
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Psychiatry and Mental Health

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