Using favorite data to analyze asymmetric competition: machine learning models

DOI10.1016/j.ejor.2020.03.074zbMath1487.91065OpenAlexW3016903886MaRDI QIDQ2023935

Jennifer Shang, Yuanchun Jiang, Yang Qian, Yezheng Liu

Publication date: 3 May 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2020.03.074

zbMATH Keywords

Dirichlet processmarket structureasymmetric competitionbipartite graph modelfavorite data

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Consumer behavior, demand theory (91B42)

Cites Work

• Unnamed Item
• Competition and market segmentation of the call center service supply chain
• Improving corporate bond recovery rate prediction using multi-factor support vector regressions
• Retail location competition under carbon penalty
• Resilient supply chain network design under competition: a case study
• Revenue management under joint pricing and capacity allocation competition
• Joint dynamic pricing and lot-sizing under competition
• Dynamic competition over social networks
• Dynamic selective maintenance optimization for multi-state systems over a finite horizon: a deep reinforcement learning approach
• Improving reliability of a shared supplier with competition and spillovers
• Spike and slab variable selection: frequentist and Bayesian strategies
• On modeling the advertising-operations interface under asymmetric competition
• Support vector regression for loss given default modelling
• Consideration Sets and Competitive Marketing
• Bayesian Variable Selection in Linear Regression
• 10.1162/jmlr.2003.3.4-5.993
• Competitive Market Structures: A Subset Selection Analysis

This page was built for publication: Using favorite data to analyze asymmetric competition: machine learning models