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科学研究
RESEARCH
Integrative High Dimensional Inference with Heterogeneity under Data Sharing Constraints
时间  Datetime
2019-07-27 14:00 — 15:00
地点  Venue
Zoom APP(2)()
报告人  Speaker
夏寅
单位  Affiliation
复旦大学
邀请人  Host
王成
备注  remarks
Zoom会议号: 952 068 71043 会议密码: 252202
报告摘要  Abstract

凯时kb88.comAbstract:Evidence based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the high dimensional setting. The challenge is even more pronounced when the individual level data cannot be shared across studies due to privacy concerns. Under ultra high dimensional sparse regression models and the constraint of not sharing individual data across studies, we propose in this talk a novel integrative estimation procedure by Aggregating and Debiasing Local Estimators (ADeLE). The ADeLE procedure protects individual data through summary-statistics-based integrating procedure, accommodates between study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Furthermore, the prediction and estimation errors incurred by aggregating derived data is negligible compared to the statistical minimax rate. In addition, the ADeLE estimator is shown to be asymptotically equivalent in prediction and estimation to the ideal estimator obtained by sharing all data. Furthermore, we propose a novel data shielding integrative large-scale testing approach to signal detection by allowing between study heterogeneity and not requiring sharing of individual level data. Assuming the underlying high dimensional regression models of the data differ across studies yet share  similar support, the proposed method incorporates proper integrative estimation and debiasing procedures to construct test statistics for the overall effects of specific covariates. We also develop a multiple testing procedure to identify significant effects while controlling the false discovery rate and false discovery proportion. The new method is applied to a real example on detecting interaction effect of the genetic variants for statins and obesity on the risk for type II diabetes.


报告人介绍:夏寅,复旦大学管理学院教授,博导。研究方向包括高维统计推断、大范围检验及应用等。在JASA, AOS, JRSSB, Biometrika等期刊上发表多篇论文。