93 citations · 248 across the 30 of their papers we have counts for
16 papers · 1 filter
Doubly Flexible Estimation under Label Shift
Seong-ho Lee, Yanyuan Ma, Jiwei Zhao
In studies ranging from clinical medicine to policy research, complete data are usually available from a population , but the quantity of interest is often sought for…
Network Autoregression for Incomplete Matrix-Valued Time Series
Xuening Zhu, Feifei Wang, Zeng Li +1
We study the dynamics of matrix-valued time series with observed network structures by proposing a matrix network autoregression model with row and column networks of the subjects.…
Matrix-valued Network Autoregression Model with Latent Group Structure
Yimeng Ren, Xuening Zhu, Ganggang Xu +1
Matrix-valued time series data are frequently observed in a broad range of areas and have attracted great attention recently. In this work, we model network effects for high dimens…
Testing for high-dimensional white noise
Long Feng, Binghui Liu, Yanyuan Ma
Testing for multi-dimensional white noise is an important subject in statistical inference. Such test in the high-dimensional case becomes an open problem waiting to be solved, esp…
Semiparametric Approach to Estimation of Marginal and Quantile Effects
Seong-ho Lee, Yanyuan Ma, Elvezio Ronchetti
We consider a semiparametric generalized linear model and study estimation of both marginal and quantile effects in this model. We propose an approximate maximum likelihood estimat…
Just Identified Indirect Inference Estimator: Accurate Inference through Bias Correction
Yuming Zhang, Yanyuan Ma, Samuel Orso +3
An important challenge in statistical analysis lies in controlling the estimation bias when handling the ever-increasing data size and model complexity of modern data settings. In…
Flexible Inference of Optimal Individualized Treatment Strategy in Covariate Adjusted Randomization with Multiple Covariates
Trinetri Ghosh, Yanyuan Ma, Rui Song +1
To maximize clinical benefit, clinicians routinely tailor treatment to the individual characteristics of each patient, where individualized treatment rules are needed and are of si…
Semi-supervised Approach to Event Time Annotation Using Longitudinal Electronic Health Records
Liang Liang, Jue Hou, Hajime Uno +3
Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to…
Learning non-monotone optimal individualized treatment regimes
Trinetri Ghosh, Yanyuan Ma, Wensheng Zhu +1
We propose a new modeling and estimation approach to select the optimal treatment regime from different options through constructing a robust estimating equation. The method is pro…
Avoid Estimating the Unknown Function in a Semiparametric Nonignorable Propensity Model
Samidha Shetty, Yanyuan Ma, Jiwei Zhao
We study the problem of estimating a functional or a parameter in the context where outcome is subject to nonignorable missingness. We completely avoid modeling the regression rela…
Covariate balancing for causal inference on categorical and continuous treatments
Seong-ho Lee, Yanyuan Ma, Xavier de Luna
We propose novel estimators for categorical and continuous treatments by using an optimal covariate balancing strategy for inverse probability weighting. The resulting estimators a…
Inference in High-Dimensional Linear Measurement Error Models
Mengyan Li, Runze Li, Yanyuan Ma
For a high-dimensional linear model with a finite number of covariates measured with error, we study statistical inference on the parameters associated with the error-prone covaria…
A Versatile Estimation Procedure without Estimating the Nonignorable Missingness Mechanism
Jiwei Zhao, Yanyuan Ma
We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable a…
Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect
Trinetri Ghosh, Yanyuan Ma, Xavier de Luna
When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mecha…
Semiparametric Mixture Regression with Unspecified Error Distributions
Yanyuan Ma, Shaoli Wang, Lin Xu +1
In fitting a mixture of linear regression models, normal assumption is traditionally used to model the error and then regression parameters are estimated by the maximum likelihood…
A Paradigmatic Regression Algorithm for Gene Selection Problems
Stéphane Guerrier, Nabil Mili, Roberto Molinari +3
Motivation: Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can…