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Found 20 papers, 1 papers with code
Two-stage Hypothesis Tests for Variable Interactions with FDR Control
In many scenarios such as genome-wide association studies where dependences between variables commonly exist, it is often of interest to infer the interaction effects in the model.
Treatment Effect Estimation with Unobserved and Heterogeneous Confounding Variables
The estimation of the treatment effect is often biased in the presence of unobserved confounding variables which are commonly referred to as hidden variables.
Optimal Variable Clustering for High-Dimensional Matrix Valued Data
To extract the information from the dependence structure for clustering, we propose a new latent variable model for the features arranged in matrix form, with some unknown membership matrices representing the clusters for the rows and columns.
Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data
Motivated by functional magnetic resonance imaging (fMRI) studies, we propose a novel method for constructing brain connectivity networks with correlated replicates and latent effects.
Methodology
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning
Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis.
Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data
This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data.
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness
In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier.
Nonregular and Minimax Estimation of Individualized Thresholds in High Dimension with Binary Responses
Statistically, we show that the finite sample error bound for estimating $\theta$ in $\ell_2$ norm is $(s\log d/n)^{\beta/(2\beta+1)}$, where $d$ is the dimension of $\theta$, $s$ is the sparsity level, $n$ is the sample size and $\beta$ is the smoothness of the conditional density of $X$ given the response $Y$ and the covariates $Z$.
Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score
We first use a class of penalized M-estimators for the propensity score and outcome models.
High-Dimensional Inference for Cluster-Based Graphical Models
We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable.
Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering
This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random vector X in Rp, with correlated unobservable factors Z \in RK, with K unknown, and independent noise E. Each row of A is scaled and sparse.
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.
A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
Our main theoretical contribution is to establish a unified Z-estimation theory of confidence regions for high dimensional problems.
Local and Global Inference for High Dimensional Nonparanormal Graphical Models
Due to the presence of unknown marginal transformations, we propose a pseudo likelihood based inferential approach.
A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
Specifically, we propose a decorrelated score function to handle the impact of high dimensional nuisance parameters.
High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.
On Semiparametric Exponential Family Graphical Models
We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data.
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models.
A Likelihood Ratio Framework for High Dimensional Semiparametric Regression
(i) We develop a regularized statistical chromatography approach to infer the parameter of interest under the proposed semiparametric generalized linear model without the need of estimating the unknown base measure function.
High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Theoretically, the proposed methods achieve the same rates of convergence for both precision matrix estimation and eigenvector estimation, as if the latent variables were observed.
