ERN: Nonparametric Methods (Topic)最新文献_第4页

Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality 日内现货波动率的非参数估计:分离瞬时趋势和季节性

ERN: Nonparametric Methods (Topic) Pub Date : 2015-08-19 DOI: 10.2139/ssrn.2330159

Thibault Vatter, Hau‐Tieng Wu, V. Chavez-Demoulin, Bin Yu

引用次数: 6

Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning 基于非参数需求学习的协调定价与库存补充

ERN: Nonparametric Methods (Topic) Pub Date : 2015-06-26 DOI: 10.2139/ssrn.2694633

Boxiao Chen, X. Chao, Hyun-Soo Ahn

{"title":"Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning","authors":"Boxiao Chen, X. Chao, Hyun-Soo Ahn","doi":"10.2139/ssrn.2694633","DOIUrl":"https://doi.org/10.2139/ssrn.2694633","url":null,"abstract":"We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price-dependent, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity, but it knows neither the form of demand dependency on selling price nor the distribution of demand uncertainty a priori, hence it has to make pricing and ordering decisions based on historical demand data. We propose a nonparametric data-driven policy that learns about the demand on the fly and, concurrently, applies learned information to determine replenishment and pricing decisions. The policy integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with the least possible profit loss. Besides convergence of optimal policies, we show that the regret, defined as the average profit loss compared with that of the optimal solution when the firm has complete information about the underlying demand, vanishes at the fastest possible rate as the planning horizon increases.","PeriodicalId":11744,"journal":{"name":"ERN: Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74649793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 60

Identification of Nonparametric Simultaneous Equations Models with a Residual Index Structure 含残差指标结构的非参数联立方程模型辨识

ERN: Nonparametric Methods (Topic) Pub Date : 2015-06-26 DOI: 10.2139/ssrn.2898841

Steven T. Berry, Philip A. Haile

引用次数: 20

A Simple Econometric Approach for Modeling Stress Event Intensities 模拟应力事件强度的简单计量经济学方法

ERN: Nonparametric Methods (Topic) Pub Date : 2015-04-01 DOI: 10.1002/FUT.21695

Rainer Jobst, D. Roesch, Harald Scheule, M. Schmelzle

引用次数: 1

Classification of Nonparametric Regression Functions in Heterogeneous Panels 非参数回归函数在异质面板中的分类

ERN: Nonparametric Methods (Topic) Pub Date : 2015-02-19 DOI: 10.2139/ssrn.2567312

M. Vogt, O. Linton

引用次数: 9

Stochastic Population Analysis: A Functional Data Approach 随机总体分析:一种功能数据方法

ERN: Nonparametric Methods (Topic) Pub Date : 2015-02-01 DOI: 10.2139/ssrn.2630301

Lei Fang, W. Härdle

引用次数: 8

Persistent Impact of Macroeconomic Announcements in Financial Market Data 宏观经济公告对金融市场数据的持续影响

ERN: Nonparametric Methods (Topic) Pub Date : 2015-01-05 DOI: 10.2139/ssrn.2556336

Nicolas Boitout, R. Lupu

引用次数: 0

Adaptive Parametric and Nonparametric Multi-Product Pricing via Self-Adjusting Controls 基于自调整控制的自适应参数和非参数多产品定价

ERN: Nonparametric Methods (Topic) Pub Date : 2014-12-01 DOI: 10.2139/SSRN.2533468

Qi (George) Chen, Stefanus Jasin, Izak Duenyas

{"title":"Adaptive Parametric and Nonparametric Multi-Product Pricing via Self-Adjusting Controls","authors":"Qi (George) Chen, Stefanus Jasin, Izak Duenyas","doi":"10.2139/SSRN.2533468","DOIUrl":"https://doi.org/10.2139/SSRN.2533468","url":null,"abstract":"We study a multi-period network revenue management (RM) problem where a seller sells multiple products made from multiple resources with finite capacity in an environment where the demand function is unknown a priori. The objective of the seller is to jointly learn the demand and price the products to minimize his expected revenue loss. Both the parametric and the nonparametric cases are considered in this paper. It is widely known in the literature that the revenue loss of any pricing policy under either case is at least k^{1/2} However, there is a considerable gap between this lower bound and the performance bound of the best known heuristic in the literature. To close the gap, we develop several self-adjusting heuristics with strong performance bound. For the general parametric case, our proposed Parametric Self-adjusting Control (PSC) attains a O(k^{1/2}) revenue loss, matching the theoretical lower bound. If the parametric demand function family further satisfies a well-separated condition, by taking advantage of passive learning, our proposed Accelerated Parametric Self-adjusting Control achieves a much sharper revenue loss of O(log^2 k). For the nonparametric case, our proposed Nonparametric Self-adjusting Control (NSC) obtains a revenue loss of O(k^{1/2+系} log k) for any arbitrarily small 系 > 0 if the demand function is sufficiently smooth. Our results suggest that in terms of performance, the nonparametric approach can be as robust as the parametric approach, at least asymptotically. All the proposed heuristics are computationally very efficient and can be used as a baseline for developing more sophisticated heuristics for large-scale problems.","PeriodicalId":11744,"journal":{"name":"ERN: Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89742085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A Nonparametric Partially Identified Estimator for Equivalence Scales 等价尺度的非参数部分辨识估计量

ERN: Nonparametric Methods (Topic) Pub Date : 2014-11-01 DOI: 10.2139/ssrn.2566241

C. Dudel

引用次数: 2

On the Validity of the Pairs Bootstrap for Lasso Estimators Lasso估计对自举的有效性

ERN: Nonparametric Methods (Topic) Pub Date : 2014-10-01 DOI: 10.2139/ssrn.2443728

Lorenzo Camponovo

引用次数: 26