Positive and Unlabeled Data: Model, Estimation, Inference, and Classification

• 成果类型: Article; Early Access
• 署名作者: Liu, Siyan; Yeh, Chi-Kuang; Zhang, Xin; Tian, Qinglong; Li, Pengfei
• 署名单位: East China Normal University; University of Waterloo
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2474266
• 发表日期: 2025
• 关键词: empirical likelihood; Covariate Shift

摘要

This study introduces a new approach to addressing the positive and unlabeled (PU) data through the double exponential tilting model (DETM) under a transfer learning framework. Traditional methods often fall short because they only apply to the common distributions (CD) PU data (also known as the selected completely at random PU data), where the labeled positive and unlabeled positive data are assumed to be from the same distribution. In contrast, our DETM's dual structure effectively accommodates the more complex and underexplored different distribution (DD) PU data (also known as the selected at random PU data), where the labeled and unlabeled positive data can be from different distributions. We rigorously establish the theoretical foundations of DETM, including identifiability, parameter estimation, and asymptotic properties. Additionally, we move forward to statistical inference by developing a goodness-of-fit test for the CD assumption and constructing confidence intervals for the proportion of positive instances in the target domain. We leverage an approximated Bayes classifier for classification tasks, demonstrating DETM's robust performance in prediction. Through theoretical insights and practical applications, this study highlights DETM as a comprehensive framework for addressing the challenges of PU data.Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.