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20162021
most citedRobust Estimation of Propensity Score Weights via Subclassification

6 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2021★ 1 cited

Empirical likelihood inference for longitudinal data with covariate measurement errors: An application to the LEAN study

Yuexia Zhang, Guoyou Qin, Zhongyi Zhu +1

Measurement errors usually arise during the longitudinal data collection process. Ignoring the effects of measurement errors will lead to invalid estimates. The Lifestyle Education…

stat.AP2021

Inverse Probability Weighting-based Mediation Analysis for Microbiome Data

Yuexia Zhang, Jian Wang, Jiayi Shen +4

Motivation: Mediation analysis is an important tool for elucidating causal mechanisms in biomedical research and has recently gained increasing attention in microbiome studies. Mot…

stat.ME2021

Weak signal identification and inference in penalized likelihood models for categorical responses

Yuexia Zhang, Peibei Shi, Zhongyi Zhu +2

Penalized likelihood models are widely used to simultaneously select variables and estimate model parameters. However, the existence of weak signals can lead to inaccurate variable…

stat.ME2020★ 1 cited

Estimation of local treatment effects under the binary instrumental variable model

Linbo Wang, Yuexia Zhang, Thomas S. Richardson +1

Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often…

math.ST2020★ 1 cited

Conditional Independence Beyond Domain Separability: Discussion of Engelke and Hitz (2020)

Yuexia Zhang, Linbo Wang

We congratulate Engelke and Hitz on a thought-provoking paper on graphical models for extremes. A key contribution of the paper is the introduction of a novel definition of conditi…

stat.ME2016★ 6 cited

Robust Estimation of Propensity Score Weights via Subclassification

Linbo Wang, Yuexia Zhang, Thomas S. Richardson +1

Weighting estimators based on propensity scores are widely used for causal estimation in a variety of contexts, such as observational studies, marginal structural models and interf…