activity
20172020
most citedIntegrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints

11 citations · 13 across the 4 of their papers we have counts for

collaborators

6 papers

stat.ML2020

Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling

Jessica Gronsbell, Molei Liu, Lu Tian +1

In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised…

stat.ME20202 cited

Double/Debiased Machine Learning for Logistic Partially Linear Model

Molei Liu, Yi Zhang, Doudou Zhou

We propose double/debiased machine learning approaches to infer (at the parametric rate) the parametric component of a logistic partially linear model with the binary response foll…

stat.ME2020

A Note on Debiased/Double Machine Learning Logistic Partially Linear Model

Molei Liu

It is of particular interests in many application fields to draw doubly robust inference of a logistic partially linear model with the predictor specified as combination of a targe…

stat.ME202011 cited

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints

Molei Liu, Yin Xia, Kelly Cho +1

Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensio…

stat.ME2019

Individual Data Protected Integrative Regression Analysis of High-dimensional Heterogeneous Data

Tianxi Cai, Molei Liu, Yin Xia

Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of…

stat.ME2017

Modeling Coefficient Alpha for Measurement of Individualized Test Score Internal Consistency

Molei Liu, Ming Hu, Xiaohua Zhou

A method for measuring individualized reliability of several tests on subjects with heterogenecity is proposed. A regression model is developed based on three sets of generalized e…