activity
20082022
most citedComment: Understanding OR, PS and DR

72 citations · 132 across the 17 of their papers we have counts for

collaborators

19 papers

stat.CO2022

Block-wise Primal-dual Algorithms for Large-scale Doubly Penalized ANOVA Modeling

Penghui Fu, Zhiqiang Tan

For multivariate nonparametric regression, doubly penalized ANOVA modeling (DPAM) has recently been proposed, using hierarchical total variations (HTVs) and empirical norms as pena…

stat.ME20221 cited

Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation

Zhiqiang Tan

Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we pr…

stat.ME2022

Imputation Maximization Stochastic Approximation with Application to Generalized Linear Mixed Models

Zexi Song, Zhiqiang Tan

Generalized linear mixed models are useful in studying hierarchical data with possibly non-Gaussian responses. However, the intractability of likelihood functions poses challenges…

stat.ME20221 cited

High-dimensional model-assisted inference for treatment effects with multi-valued treatments

Wenfu Xu, Zhiqiang Tan

Consider estimation of average treatment effects with multi-valued treatments using augmented inverse probability weighted (IPW) estimators, depending on outcome regression and pro…

stat.CO2021

On Irreversible Metropolis Sampling Related to Langevin Dynamics

Zexi Song, Zhiqiang Tan

There has been considerable interest in designing Markov chain Monte Carlo algorithms by exploiting numerical methods for Langevin dynamics, which includes Hamiltonian dynamics as…

stat.ME20213 cited

Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data

Peng Wu, Zhiqiang Tan, Wenjie Hu +1

Covariate-specific treatment effects (CSTEs) represent heterogeneous treatment effects across subpopulations defined by certain selected covariates. In this article, we consider ma…