60 citations · 266 across the 46 of their papers we have counts for
22 papers · 1 filter
Scalable Statistical Inference in Non-parametric Least Squares
Meimei Liu, Zuofeng Shang, Yun Yang
Stochastic approximation (SA) is a powerful and scalable computational method for iteratively estimating the solution of optimization problems in the presence of randomness, partic…
Minimax optimal high-dimensional classification using deep neural networks
Shuoyang Wang, Zuofeng Shang
High-dimensional classification is a fundamentally important research problem in high-dimensional data analysis. In this paper, we derive a nonasymptotic rate for the minimax exces…
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Tianyang Hu, Ruiqi Liu, Zuofeng Shang +1
Deep learning has gained huge empirical successes in large-scale classification problems. In contrast, there is a lack of statistical understanding about deep learning methods, par…
Information-theoretic Limits for Testing Community Structures in Weighted Networks
Mingao Yuan, Zuofeng Shang
Community detection refers to the problem of clustering the nodes of a network into groups. Existing inferential methods for community structure mainly focus on unweighted (binary)…
Statistical Inference for Functional Linear Quantile Regression
Peijun Sang, Zuofeng Shang, Pang Du
We propose inferential tools for functional linear quantile regression where the conditional quantile of a scalar response is assumed to be a linear functional of a functional cova…
Statistical Limits for Testing Correlation of Hypergraphs
Mingao Yuan, Zuofeng Shang
In this paper, we consider the hypothesis testing of correlation between two -uniform hypergraphs on unlabelled nodes. Under the null hypothesis, the hypergraphs are indepen…
Sharp detection boundaries on testing dense subhypergraph
Mingao Yuan, Zuofeng Shang
We study the problem of testing the existence of a dense subhypergraph. The null hypothesis is an Erdos-Renyi uniform random hypergraph and the alternative hypothesis is a uniform…
A Computationally Efficient Classification Algorithm in Posterior Drift Model: Phase Transition and Minimax Adaptivity
Ruiqi Liu, Kexuan Li, Zuofeng Shang
In massive data analysis, training and testing data often come from very different sources, and their probability distributions are not necessarily identical. A feature example is…
On Deep Instrumental Variables Estimate
Ruiqi Liu, Zuofeng Shang, Guang Cheng
The endogeneity issue is fundamentally important as many empirical applications may suffer from the omission of explanatory variables, measurement error, or simultaneous causality.…
Nonparametric Inference under B-bits Quantization
Kexuan Li, Ruiqi Liu, Ganggang Xu +1
Statistical inference based on lossy or incomplete samples is often needed in research areas such as signal/image processing, medical image storage, remote sensing, signal transmis…
Testing Community Structures for Hypergraphs
Mingao Yuan, Ruiqi Liu, Yang Feng +1
Many complex networks in real world can be formulated as hypergraphs where community detection has been widely used. However, the fundamental question of whether communities exist…
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?
Meimei Liu, Zuofeng Shang, Guang Cheng
This paper aims to solve a basic problem in distributed statistical inference: how many machines can we use in parallel computing? In kernel ridge regression, we address this quest…
Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method
Shunan Zhao, Ruiqi Liu, Zuofeng Shang
We propose statistical inferential procedures for panel data models with interactive fixed effects in a kernel ridge regression framework.Compared with traditional sieve methods, o…
Non-asymptotic theory for nonparametric testing
Yun Yang, Zuofeng Shang, Guang Cheng
We consider nonparametric testing in a non-asymptotic framework. Our statistical guarantees are exact in the sense that Type I and II errors are controlled for any finite sample si…
A maximum smoothed likelihood based estimation for two component semiparametric density mixtures with a known component
Zhou Shen, Michael Levine, Zuofeng Shang
We consider a semiparametric mixture of two univariate density functions where one of them is known while the weight and the other function are unknown. Such mixtures have a histor…
Computational Limits of A Distributed Algorithm For Smoothing Spline
Zuofeng Shang, Guang Cheng
In this paper, we explore statistical versus computational trade-off to address a basic question in the application of a distributed algorithm: what is the minimal computational co…
Nonparametric Bayesian Aggregation for Massive Data
Zuofeng Shang, Botao Hao, Guang Cheng
We develop a set of scalable Bayesian inference procedures for a general class of nonparametric regression models. Specifically, nonparametric Bayesian inferences are separately pe…
Gaussian Approximation of General Nonparametric Posterior Distributions
Zuofeng Shang, Guang Cheng
In a general class of Bayesian nonparametric models, we prove that the posterior distribution can be asymptotically approximated by a Gaussian process. Our results apply to nonpara…
Nonparametric inference in generalized functional linear models
Zuofeng Shang, Guang Cheng
We propose a roughness regularization approach in making nonparametric inference for generalized functional linear models. In a reproducing kernel Hilbert space framework, we const…
Joint asymptotics for semi-nonparametric regression models with partially linear structure
Guang Cheng, Zuofeng Shang
We consider a joint asymptotic framework for studying semi-nonparametric regression models where (finite-dimensional) Euclidean parameters and (infinite-dimensional) functional par…
Local and global asymptotic inference in smoothing spline models
Zuofeng Shang, Guang Cheng
This article studies local and global inference for smoothing spline estimation in a unified asymptotic framework. We first introduce a new technical tool called functional Bahadur…
Consistency of Bayesian Linear Model Selection With a Growing Number of Parameters
Zuofeng Shang, Murray K. Clayton
Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesi…