Factor-adjusted tests for generalized linear models with multimodal data:An application to breast cancer data  

作　　者：Dongyu Li Lei Wang 

机构地区：[1]School of Statistics and Data Science,KLMDASR,LEBPS,and LPMC,Nankai University,Tianjin 300071,China

出　　处：《Science China Mathematics》2025年第2期447-484,共38页中国科学(数学英文版)

基　　金：supported by the Fundamental Research Funds for the Central Universities;National Natural Science Foundation of China(Grant No.12271272)。

摘　　要：With the advancement of modern scientific research,multimodal data is increasingly being collected from multiple sources or types.For outcomes derived from generalized linear models with high-dimensional and multimodal covariates,we develop two distinct factor-adjusted tests to assess the significance of high-dimensional modality data and specific low-dimensional linear combinations of predictors from one or more modalities,respectively.First,we propose a factor-adjusted decorrelated score test to evaluate the significance of a single modality.This approach simultaneously transforms a high-dimensional test into a fixed low-dimensional one while addressing the impact of high-dimensional nuisance parameters.Second,we construct a factor-adjusted Wald test based on partial penalized estimation to assess the significance of certain low-dimensional combinations of variables from one or more modalities.The limiting distributions of these two proposed tests are analyzed under both the null hypothesis and local alternatives to characterize the asymptotic type-I errors and powers.The finite sample performance of our proposed tests is evaluated through simulations and further demonstrated with a breast cancer dataset.

关 键 词：decorrelated score factor model high-dimensional data integrative analysis partial penalized Wald test 

分 类 号：O212.1[理学—概率论与数理统计] R737.9[理学—数学]