A Likelihood-Based Approach for Multivariate Categorical Response Regression in High Dimensions

• 成果类型: Article
• 署名作者: Molstad, Aaron J.; Rothman, Adam J.
• 署名单位: State University System of Florida; University of Florida; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1999819
• 发表日期: 2023
• 页码: 1402-1414
• 关键词: binary relevance

摘要

We propose a penalized likelihood method to fit the bivariate categorical response regression model. Our method allows practitioners to estimate which predictors are irrelevant, which predictors only affect the marginal distributions of the bivariate response, and which predictors affect both the marginal distributions and log odds ratios. To compute our estimator, we propose an efficient algorithm which we extend to settings where some subjects have only one response variable measured, that is, a semi-supervised setting. We derive an asymptotic error bound which illustrates the performance of our estimator in high-dimensional settings. Generalizations to the multivariate categorical response regression model are proposed. Finally, simulation studies and an application in pan-cancer risk prediction demonstrate the usefulness of our method in terms of interpretability and prediction accuracy. for this article are available online.