72 citations · 159 across the 25 of their papers we have counts for
25 papers
Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies
Zhiqiang Tan
Consider sensitivity analysis to assess the worst-case possible values of counterfactual outcome means and average treatment effects under sequential unmeasured confounding in a lo…
Persistently Trained, Diffusion-assisted Energy-based Models
Xinwei Zhang, Zhiqiang Tan, Zhijian Ou
Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have bee…
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…
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…
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…
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…
Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models
Ziyue Wang, Zhiqiang Tan
Consider the problem of simultaneous estimation of location and variance matrix under Huber's contaminated Gaussian model. First, we study minimum -divergence estimation at the…
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…
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…
Consistent and robust inference in hazard probability and odds models with discrete-time survival data
Zhiqiang Tan
For discrete-time survival data, conditional likelihood inference in Cox's hazard odds model is theoretically desirable but exact calculation is numerical intractable with a modera…
Doubly Robust Semiparametric Inference Using Regularized Calibrated Estimation with High-dimensional Data
Satyajit Ghosh, Zhiqiang Tan
Consider semiparametric estimation where a doubly robust estimating function for a low-dimensional parameter is available, depending on two working models. With high-dimensional da…
High-dimensional Model-assisted Inference for Local Average Treatment Effects with Instrumental Variables
Baoluo Sun, Zhiqiang Tan
Consider the problem of estimating the local average treatment effect with an instrument variable, where the instrument unconfoundedness holds after adjusting for a set of measured…
Hamiltonian Assisted Metropolis Sampling
Zexi Song, Zhiqiang Tan
Various Markov chain Monte Carlo (MCMC) methods are studied to improve upon random walk Metropolis sampling, for simulation from complex distributions. Examples include Metropolis-…
On loss functions and regret bounds for multi-category classification
Zhiqiang Tan, Xinwei Zhang
We develop new approaches in multi-class settings for constructing proper scoring rules and hinge-like losses and establishing corresponding regret bounds with respect to the zero-…
Analysis of odds, probability, and hazard ratios: From 2 by 2 tables to two-sample survival data
Zhiqiang Tan
Analysis of 2 by 2 tables and two-sample survival data has been widely used. Exact calculation is computational intractable for conditional likelihood inference in odds ratio model…
Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models
Xinwei Zhang, Zhiqiang Tan
Consider semi-supervised learning for classification, where both labeled and unlabeled data are available for training. The goal is to exploit both datasets to achieve higher predi…
On doubly robust estimation for logistic partially linear models
Zhiqiang Tan
Consider a logistic partially linear model, in which the logit of the mean of a binary response is related to a linear function of some covariates and a nonparametric function of o…
Improved Methods for Moment Restriction Models with Marginally Incompatible Data Combination and an Application to Two-sample Instrumental Variable Estimation
Heng Shu, Zhiqiang Tan
Combining information from multiple samples is often needed in biomedical and economic studies, but the differences between these samples must be appropriately taken into account i…
Improved Estimation of Average Treatment Effects on the Treated: Local Efficiency, Double Robustness, and Beyond
Heng Shu, Zhiqiang Tan
Estimation of average treatment effects on the treated (ATT) is an important topic of causal inference in econometrics and statistics. This problem seems to be often treated as a s…
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data
Zhiqiang Tan
Consider the problem of estimating average treatment effects when a large number of covariates are used to adjust for possible confounding through outcome regression and propensity…
Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
Zhiqiang Tan
Propensity score methods are widely used for estimating treatment effects from observational studies. A popular approach is to estimate propensity scores by maximum likelihood base…
Penalized Estimation in Additive Regression with High-Dimensional Data
Zhiqiang Tan, Cun-Hui Zhang
Additive regression provides an extension of linear regression by modeling the signal of a response as a sum of functions of covariates of relatively low complexity. We study penal…
Improved minimax estimation of a multivariate normal mean under heteroscedasticity
Zhiqiang Tan
Consider the problem of estimating a multivariate normal mean with a known variance matrix, which is not necessarily proportional to the identity matrix. The coordinates are shrunk…
Instrumental Variable Estimation When Compliance is not Deterministic: The Stochastic Monotonicity Assumption
Dylan Small, Zhiqiang Tan, Scott Lorch +1
The instrumental variables (IV) method is a method for making causal inferences about the effect of a treatment based on an observational study in which there are unmeasured confou…