163 citations · 175 across the 6 of their papers we have counts for
7 papers
High-Dimensional Statistical Inference for Sparse Support Vector Machines
Peng Zeng, Hanwen Huang
Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample size and number of features grow…
A Bayesian Approach for Robust Longitudinal Envelope Models
Peng Zeng, Yushan Mu
The envelope model provides a dimension-reduction framework for multivariate linear regression. However, existing envelope methods typically assume normally distributed random erro…
Statistical Inference in Classification of High-dimensional Gaussian Mixture
Hanwen Huang, Peng Zeng
We consider the classification problem of a high-dimensional mixture of two Gaussians with general covariance matrices. Using the replica method from statistical physics, we invest…
Extended ADMM for general penalized quantile regression with linear constraints in big data
Yongxin Liu, Peng Zeng
Quantile regression (QR) can be used to describe the comprehensive relationship between a response and predictors. Prior domain knowledge and assumptions in application are usually…
Phase transition and higher order analysis of regularization under dependence
Hanwen Huang, Peng Zeng, Qinglong Yang
We study the problem of estimating a -sparse signal ${\mbox{$β$}}_0\in{\bf R}^p$ from a set of noisy observations under the model ${\bf y}={\bf X}{\mbox{$β…
A Model-free Variable Screening Method Based on Leverage Score
Wenxuan Zhong, Yiwen Liu, Peng Zeng
With rapid advances in information technology, massive datasets are collected in all fields of science, such as biology, chemistry, and social science. Useful or meaningful informa…
Sparse Principal Component Analysis via Variable Projection
N. Benjamin Erichson, Peng Zheng, Krithika Manohar +3
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying loc…