21 citations · 31 across the 4 of their papers we have counts for
6 papers · 1 filter
A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data
Sai Li, Ting Ye
Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Alth…
Estimation and Inference with Proxy Data and its Genetic Applications
Sai Li, T. Tony Cai, Hongzhe Li
Existing high-dimensional statistical methods are largely established for analyzing individual-level data. In this work, we study estimation and inference for high-dimensional line…
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
Sai Li, T. Tony Cai, Hongzhe Li
Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxilia…
Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality
Sai Li, T. Tony Cai, Hongzhe Li
This paper considers the estimation and prediction of a high-dimensional linear regression in the setting of transfer learning, using samples from the target model as well as auxil…
Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach
Sai Li, Tony T. Cai, Hongzhe Li
Linear mixed-effects models are widely used in analyzing clustered or repeated measures data. We propose a quasi-likelihood approach for estimation and inference of the unknown par…
Mendelian Randomization when Many Instruments are Invalid: Hierarchical Empirical Bayes Estimation
Sai Li
Estimating the causal effect of an exposure on an outcome is an important task in many economical and biological studies. Mendelian randomization, in particular, uses genetic varia…