Papers

High-dimensional covariance regression with application to co-expression QTL detection

Rakheon Kim, Jingfei Zhang

Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions

Anders B. Kock, Rasmus S. Pedersen, Jesper R.-V. Sørensen

Identifying the structure of high-dimensional time series via eigen-analysiss

Bo Zhang, Jiti Gao, Guangming Pan et al.

Higher Order Accurate Symmetric Bootstrap Confidence Intervals in High Dimensional Penalized Regression

Debraj Das, Arindam Chatterjee, S. N. Lahiri

Sparse Bayesian Multidimensional Item Response Theory

Jiguang Li, Robert Gibbons, Veronika Ročková

Communication-Efficient Distributed Sparse Learning with Oracle Property and Geometric Convergence

Weidong Liu, Xiaojun Mao, Jiyuan Tu

Statistical Inference for High-Dimensional Spectral Density Matrix

Jinyuan Chang, Qing Jiang, Tucker McElroy et al.

Frequency Domain Statistical Inference for High-Dimensional Time Series

Jonas Krampe, Efstathios Paparoditis

Kernel Meets Sieve: Transformed Hazards Models with Sparse Longitudinal Covariates

Dayu Sun, Zhuowei Sun, Xingqiu Zhao et al.

Statistical Inference for High-Dimensional Convoluted Rank Regression

Leheng Cai, Xu Guo, Heng Lian et al.

Class-Specific Joint Feature Screening in Ultrahigh-Dimensional Mixture Regression

Kaili Jing, Abbas Khalili, Chen Xu

Degree-Heterogeneous Latent Class Analysis for High-Dimensional Discrete Data

Zhongyuan Lyu, Ling Chen, Yuqi Gu

High-Dimensional Variable Clustering based on Maxima of a Weakly Dependent Random Process

Alexis Boulin, Elena Di Bernardino, Thomas Laloë et al.

High-Dimensional Expected Shortfall Regression

Shushu Zhang, Xuming He, Kean Ming Tan et al.

An Adaptive Adjustment to the R₂ Statistic in High-Dimensional Elliptical Models

Shizhe Hong, Weiming Li, Qiang Liu et al.

High-Dimensional Knockoffs Inference for Time Series Data

Chien-Ming Chi, Yingying Fan, Ching-Kang Ing et al.

Adaptive Testing for High-Dimensional Data

Yangfan Zhang, Runmin Wang, Xiaofeng Shao

Comparison of Longitudinal Trajectories Using a High-Dimensional Partial Linear Semiparametric Mixed-Effects Model

Sami Leon, Tong Tong Wu

Deconvolution Density Estimation with Penalized MLE

Yun Cai, Hong Gu, Toby Kenney

Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity

Xinmeng Huang, Kan Xu, Donghwan Lee et al.

On the Modeling and Prediction of High-Dimensional Functional Time Series

Jinyuan Chang, Qin Fang, Xinghao Qiao et al.

High-Dimensional Hilbert-Schmidt Linear Regression with Hilbert Manifold Variables

Changwon Choi, Byeong U. Park

A Geometrical Analysis of Kernel Ridge Regression and its Applications

Zong Shang, Guillaume Lecué, Georgios Gavrilopoulos

Sparse PCA: A New Scalable Estimator Based on Integer Programming

Kayhan Behdin, Rahul Mazumder

The High-Dimensional Asymptotics of Principal Component Regression

Alden Green, Elad Romanov

Advances in Bayesian Model Selection Consistency for High-Dimensional Generalized Linear Models

Jeyong Lee, Minwoo Chae, Ryan Martin

High-Dimensional Statistical Inference for Linkage Disequilibrium Score Regression and Its Cross-Ancestry Extensions

Fei Xue, Bingxin Zhao

The Functional Graphical Lasso

Kartik Govind Waghmare, Tomas Masak, Victor Michael Panaretos

Debiased Regression Adjustment in Completely Randomized Experiments with Moderately High-Dimensional Covariates

Xin Lu, Fan Yang, Yuhao Wang

Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

Fan Yang, Hongyang R. Zhang, Sen Wu et al.

The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following q...

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

Shao-Bo Lin, Xiaotong Liu, Di Wang et al.

Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for ...

Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors

Gaoyu Wu, Jelena Bradic, Kean Ming Tan et al.

Expected shortfall (ES) is widely used for characterizing the tail of a distribution across various fields, particularly in financial risk management....

Outlier Robust and Sparse Estimation of Linear Regression Coefficients

Takeyuki Sasai, Hironori Fujisawa

We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial...

High-Dimensional L2-Boosting: Rate of Convergence

Ye Luo, Martin Spindler, Jannis Kueck

Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of L2-Boosting in a high-dimensio...

Rank-one Convexification for Sparse Regression

Alper Atamturk, Andres Gomez

Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact m...

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Dapeng Yao, Fangzheng Xie, Yanxun Xu

We study the sparse high-dimensional Gaussian mixture model when the number of clusters is allowed to grow with the sample size. A minimax lower bound...

From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

Shaojun Guo, Dong Li, Xinghao Qiao et al.

Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most...

DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

Jiayi Tong, Jie Hu, George Hripcsak et al.

High-dimensional healthcare data, such as electronic health records (EHR) data and claims data, present two primary challenges due to the large number...

Simulating diffusion bridges with score matching

J Heng, others

Abstract

Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling

Xiaotong Lin, others

Detection and inference of changes in high-dimensional linear regression with nonsparse structures

Haeran Cho, others

An optimal design framework for lasso sign recovery

Jonathan W Stallrich, others

Dynamic Factor Analysis of High-Dimensional Recurrent Events

F Chen, others

Abstract

Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models

Tate Jacobson

Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling

Jinyuan Chang, others

High-dimensional Factor Analysis for Network-linked data

Jinming Li, others

Abstract