GENERALIZED THEME DICTIONARY MODELS FOR ASSOCIATION PATTERN DISCOVERY

• 成果类型: Article
• 署名作者: Yang, By yang; Deng, K. E.
• 署名单位: Nankai University; Tsinghua University; Tsinghua University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1626
• 发表日期: 2023
• 页码: 269-293
• 关键词: traditional chinese medicine; maximum-likelihood; algorithms

摘要

Discovering association patterns of items from a collection of baskets composed of different items is an important problem in various fields. Assum-ing that each basket is composed of themes of items randomly sampled from a theme dictionary, the theme dictionary model provides a general framework to achieve efficient association pattern discovery with statistical inference. This paper extends the original theme dictionary model by allowing more than one category of items in a basket and only presence/absence of items is observed for each basket with all quantitative information missing. The ex-tended models can solve a larger range of practical problems that cannot be handled by the original theme dictionary model. Both simulation studies and real data applications confirm the superiority of the proposed methods over the existing ones.