Policy Optimization Using Semiparametric Models for Dynamic Pricing

Fan, Jianqing; Guo, Yongyi; Yu, Mengxin

doi:10.1080/01621459.2022.2128359

Cited by 9 publications

(1 citation statement)

References 55 publications

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“…Dynamic pricing has been another important stream in revenue management and the pricedemand curve is often assumed to be linear (Kleinberg and Leighton 2003, Araman and Caldentey 2009, Besbes and Zeevi 2009, Broder and Rusmevichientong 2012, den Boer and Zwart 2014, Keskin and Zeevi 2014; the paper by Den Boer (2015) provides a helpful survey. Recent work has turned towards dynamic pricing based on customer characteristics (e.g., Ban and Keskin (2021), Chen and Gallego (2021), Bastani et al (2022)) and/or product features (e.g., Qiang and Bayati (2016), Javanmard and Nazerzadeh (2019), Cohen et al (2020), , Fan et al (2022)). Much of the pricing literature focuses on the single-product setting, while sellers usually need to price multiple products simultaneously.…”

Section: Related Literaturementioning

confidence: 99%

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing

Cai,

Chen,

Wainwright

et al. 2023

SSRN Journal

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Section: Related Literaturementioning

confidence: 99%

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing

Cai,

Chen,

Wainwright

et al. 2023

SSRN Journal

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COMBSS: best subset selection via continuous optimization

Moka,

Liquet,

Zhu

et al. 2024

Stat Comput

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The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very large compared to the number of data samples. Existing optimal methods for solving this problem tend to be slow while fast methods tend to have low accuracy. Ideally, new methods perform best subset selection faster than existing optimal methods but with comparable accuracy, or, being more accurate than methods of comparable computational speed. Here, we propose a novel continuous optimization method that identifies a subset solution path, a small set of models of varying size, that consists of candidates for the single best subset of features, that is optimal in a specific sense in linear regression. Our method turns out to be fast, making the best subset selection possible when the number of features is well in excess of thousands. Because of the outstanding overall performance, framing the best subset selection challenge as a continuous optimization problem opens new research directions for feature extraction for a large variety of regression models.

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Exploring the sustainable growth pathway of wind power in China: Using the semiparametric regression model

2023

Energy Policy

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Policy Optimization Using Semiparametric Models for Dynamic Pricing

Cited by 9 publications

References 55 publications

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing

COMBSS: best subset selection via continuous optimization

Exploring the sustainable growth pathway of wind power in China: Using the semiparametric regression model

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