无需调参的动态定价方法，提升医疗地产收益
Dynamic Pricing in the Linear Valuation Model using Shape Constraints
- 基于保序回归与弱光滑性假设，处理带截断数据的动态定价
- 实测与模拟均显示比现有方法更低的经验损失
- 适合追求免调参、高稳定性的工业级定价场景
针对线性估值模型中存在截断数据的动态定价问题，本文提出一种形状约束方法，无需依赖传统方法所需的调参过程。以往研究多假设市场噪声分布 $F_0$ 满足Lipschitz（或更强）条件，采用核方法或强化学习策略（如多臂赌博机与上置信界算法）。本文则在更弱的 $α$-Hölder连续性假设下（$α∈(0,1]$），基于保序回归推导出后悔上界。通过仿真及来自Welltower Inc（一家大型医疗地产信托公司）的真实数据实验，结果一致表明，该方法在多种场景下均实现低于现有方法的经验后悔值，且具备无需调参的优势。
原文摘要 · Abstract (English)
We propose a shape-constrained approach to dynamic pricing for censored data in the linear valuation model eliminating the need for tuning parameters commonly required by existing methods. Previous works have addressed the challenge of unknown market noise distribution $F_0$ using strategies ranging from kernel methods to reinforcement learning algorithms, such as bandit techniques and upper confidence bounds (UCB), under the assumption that $F_0$ satisfies Lipschitz (or stronger) conditions. In contrast, our method relies on isotonic regression under the weaker assumption that $F_0$ is $α$-Hölder continuous for some $α\in (0,1]$, for which we derive a regret upper bound. Simulations and experiments with real-world data obtained by Welltower Inc (a major healthcare Real Estate Investment Trust) consistently demonstrate that our method attains lower empirical regret in comparison to several existing methods in the literature while offering the advantage of being tuning-parameter free.
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