Publications (78)
High dimensional alpha test for linear factor pricing model with -norm
Ping Zhao, Huifang Ma, Long Feng
We consider testing zero pricing errors in high-dimensional linear factor pricing models. Existing methods are mainly based on either an statistic, which is effective under d…
A Goodness-of-Fit Test for Sparse Networks
Yujia Wu, Wei Lan, Long Feng +1
The stochastic block model (SBM) has been widely used to analyze network data. Various goodness-of-fit tests have been proposed to assess the adequacy of model structures. To the b…
Testing Alpha in High-Dimensional Conditional Time-Varying Factor Models with Dependent Observations
Long Feng, Huifang Ma, Zhaojun Wang
This paper studies alpha testing in a high-dimensional conditional time-varying factor model with temporally dependent observations. Both factor loadings and alpha processes are al…
A Nonparametric Statistics Approach to Feature Selection in Deep Neural Networks with Theoretical Guarantees
Junye Du, Zhenghao Li, Zhutong Gu +1
This paper tackles the problem of feature selection in a highly challenging setting: , where is…
Testing Alpha in High Dimensional Linear Factor Pricing Models with Dependent Observations
Huifang Ma, Long Feng, Zhaojun Wang +1
In this study, we introduce three distinct testing methods for testing alpha in high dimensional linear factor pricing model that deals with dependent data. The first method is a s…
Adaptive Testing for Alphas in Conditional Factor Models with High Dimensional Assets
Huifang MA, Long Feng, Zhaojun Wang
This paper focuses on testing for the presence of alpha in time-varying factor pricing models, specifically when the number of securities N is larger than the time dimension of the…
High-Dimensional Hettmansperger-Randles Estimator and its Applications
Guowei Yan, Long Feng, Xiaoxu Zhang
The classic Hettmansperger-Randles Estimator has found extensive use in robust statistical inference. However, it cannot be directly applied to high-dimensional data. In this paper…
Adaptive Rank-based Tests for High Dimensional Mean Problems
Yu Zhang, Long Feng
The Wilcoxon signed-rank test and the Wilcoxon-Mann-Whitney test are commonly employed in one sample and two sample mean tests for one-dimensional hypothesis problems. For high-dim…
Elliptical Regularized Hotelling Tests for High-Dimensional Change-Point Detection
Fengyi Song, Mengtao Wen, Long Feng
We propose an elliptical regularized Hotelling (ERHT) procedure for detecting location changes in high-dimensional sequences with heavy-tailed, cross-sectionally dependent observat…
High Dimensional Sparse Canonical Correlation Analysis for Elliptical Symmetric Distributions
Chengde Qian, Yanhong Liu, Long Feng
This paper proposes a robust high-dimensional sparse canonical correlation analysis (CCA) method for investigating linear relationships between two high-dimensional random vectors,…
FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing
Junye Du, Zhenghao Li, Yushi Feng +1
Federated learning with heterogeneous clients remains a significant challenge for deep learning, primarily due to client drift arising from inconsistent local updates. Existing fed…
Semiparametric Elliptical Mixture Clustering for High-Dimensional Data
Long Feng, Dan Zhuang
Clustering high-dimensional data is especially challenging when cluster distributions are heavy tailed and only approximately elliptical. Existing high-dimensional methods are larg…
Factor-Adjusted Location Tests for High-Dimensional Time Series
Jiyang Wang, Xifen Huang, Long Feng
We study high-dimensional one-sample mean testing for time series with strong common serial dependence driven by latent dynamic factors. After estimating the dynamic factor loading…
A Note on High Dimensional Two Sample Mean Test
Long Feng, Fasheng Sun
In this paper, we propose a new scalar and shift transform invariant test statistic for the high-dimensional two-sample location test. The expectation of our test is exactly zero u…
Sparse -spatial-median clustering for high-dimensional data
Ping Zhao, Dan Zhuang, Long Feng
We propose a robust clustering framework for high-dimensional data with heavy tails and a large fraction of irrelevant variables. The method replaces the mean updates of Lloyd's $K…
A Spatial-Sign based Direct Approach for High Dimensional Sparse Quadratic Discriminant Analysis
Anqing Shen, Long Feng
In this paper, we study the problem of high-dimensional sparse quadratic discriminant analysis (QDA). We propose a novel classification method, termed SSQDA, which is constructed v…
Nonlinear Multiple Response Regression and Learning of Latent Spaces
Ye Tian, Sanyou Wu, Long Feng
Identifying low-dimensional latent structures within high-dimensional data has long been a central topic in the machine learning community, driven by the need for data compression,…
Adaptive Change Point Inference for High Dimensional Time Series with Temporal Dependence
Xiaoyi Wang, Jixuan Liu, Long Feng
This paper investigates change point inference in high-dimensional time series. We begin by introducing a max--norm based test procedure, which demonstrates strong performance…
High dimensional Mean Test for Temporal Dependent Data
Yuchen Hu, Xiaoyi Wang, Long Feng
This paper proposes a novel test method for high-dimensional mean testing regard for the temporal dependent data. Comparison to existing methods, we establish the asymptotic normal…
Rank-Based Tests for Mutual Independence of High-Dimensional Random Vectors via Norm
Ping Zhao, Hongfei Wang, Long Feng
We consider the problem of testing mutual independence among the components of a high-dimensional random vector. Building on the rank-based max-sum framework, we introduce fixed fi…
Data Privatization in Vertical Federated Learning with Client-wise Missing Problem
Huiyun Tang, Long Feng, Yang Li +1
Vertical Federated Learning (VFL) often suffers from client-wise missingness, where entire feature blocks from some clients are unobserved, and conventional approaches are vulnerab…
Robust Mutual Fund Selection with False Discovery Rate Control
Hongfei Wang, Long Feng, Ping Zhao +1
In this article, we address the challenge of identifying skilled mutual funds among a large pool of candidates, utilizing the linear factor pricing model. Assuming observable facto…
Facial Foundational Model Advances Early Warning of Coronary Artery Disease from Live Videos with DigitalShadow
Juexiao Zhou, Zhongyi Han, Mankun Xin +19
Global population aging presents increasing challenges to healthcare systems, with coronary artery disease (CAD) responsible for approximately 17.8 million deaths annually, making…
High Dimensional Spatial Rank Test for Two-Sample Location Problem
Long Feng
This article concerns tests for the two-sample location problem when the dimension is larger than the sample size. The traditional multivariate-rank-based procedures cannot be used…
High-Dimensional Two-Sample Test for Elliptical Symmetry Distribution
Long Feng, Hongfei Wang
We study the high-dimensional two-sample location problem under elliptical symmetry with arbitrary dependence in the scatter matrix. Existing spatial-sign procedures are attractive…
Rank Based Tests for High Dimensional White Noise
Dachuan Chen, Fengyi Song, Long Feng
The development of high-dimensional white noise test is important in both statistical theories and applications, where the dimension of the time series can be comparable to or exce…
High dimensional matrix estimation through elliptical factor models
Xinyue Xu, Huifang Ma, Hongfei Wang +1
Elliptical factor models play a central role in modern high-dimensional data analysis, particularly due to their ability to capture heavy-tailed and heterogeneous dependence struct…
Semi-Supervised Generative Learning via Latent Space Distribution Matching
Kwong Yu Chong, Long Feng
We introduce Latent Space Distribution Matching (LSDM), a novel framework for semi-supervised generative modeling of conditional distributions. LSDM operates in two stages: (i) lea…
Asymptotic Independence of the Quadratic form and Maximum of Independent Random Variables with Applications to High-Dimensional Tests
Dachuan Chen, Decai Liang, Long Feng
This paper establishes the asymptotic independence between the quadratic form and maximum of a sequence of independent random variables. Based on this theoretical result, we find t…
Approximate nonparametric maximum likelihood inference for mixture models via convex optimization
Long Feng, Lee H. Dicker
Nonparametric maximum likelihood (NPML) for mixture models is a technique for estimating mixing distributions that has a long and rich history in statistics going back to the 1950s…
Enterprise Profit Prediction Using Multiple Data Sources with Missing Values through Vertical Federated Learning
Huiyun Tang, Feifei Wang, Long Feng +1
Small and medium-sized enterprises (SMEs) play a crucial role in driving economic growth. Monitoring their financial performance and discovering relevant covariates are essential f…
Adaptive Sphericity Tests for High Dimensional Data
Ping Zhao, Wenwan Yang, Long Feng +1
In this paper, we investigate sphericity testing in high-dimensional settings, where existing methods primarily rely on sum-type test procedures that often underperform under spars…
Cauchy Aggregation of Ridge-Regularized Hotelling Tests for High-Dimensional Change-Point Detection
Ping Zhao, Le Zhou, Long Feng
Ridge-regularized Hotelling-type (RHT) change-point tests depend on a ridge parameter , but the power-optimal value is determined by the unknown covariance structure and the un…
High Dimensional Rank Tests for Sphericity
Long Feng
Sphericity test plays a key role in many statistical problems. We propose Spearman's rho-type rank test and Kendall's tau-type rank test for sphericity in the high dimensional sett…
High-Dimensional Data Analysis for Elliptically Symmetric Distributions
Long Feng
High-dimensional data arise routinely in modern statistics, econometrics, finance, genomics, and machine learning. While a large body of existing methodology is developed under Gau…
Spatial-Sign based High dimensional Change Point Inference
Jixuan Liu, Long Feng, Liuhua Peng +1
High-dimensional changepoint inference, adaptable to diverse alternative scenarios, has attracted significant attention in recent years. In this paper, we propose an adaptive and r…
Conditional Rank-Rank Regression via Deep Conditional Transformation Models
Xiaoyi Wang, Long Feng, Zhaojun Wang
Intergenerational mobility quantifies the transmission of socio-economic outcomes from parents to children. While rank-rank regression (RRR) is standard, adding covariates directly…
Adaptive L-statistics for high dimensional test problem
Huifang Ma, Long Feng, Zhaojun Wang
In this study, we focus on applying L-statistics to the high-dimensional one-sample location test problem. Intuitively, an L-statistic with parameters tends to perform optimall…
Note on High Dimensional Spatial-Sign Test for One Sample Problem
Ping Zhao, Long Feng
We revisit the null distribution of the high-dimensional spatial-sign test of Wang et al. (2015) under mild structural assumptions on the scatter matrix. We show that the standardi…
High-Dimensional Tests for Elliptical Models via Radial--Directional Dependence
Haoran Zhang, Long Feng
We develop high-dimensional goodness-of-fit tests for elliptical models by testing radial--directional independence after affine standardization. The method forms coordinatewise co…
Difference-Based High-Dimensional Long-Run Covariance Matrix Estimation for Mean-shift Time Series
Yanhong Liu, Fengyi Song, Long Feng
We consider estimation of high-dimensional long-run covariance matrices for time series with nonconstant means, a setting in which conventional estimators can be severely biased. T…
Double Robust high dimensional alpha test for linear factor pricing model
Ping Zhao, Long Feng, Hongfei Wang +1
In this paper, we investigate alpha testing for high-dimensional linear factor pricing models. We propose a spatial sign-based max-type test to handle sparse alternative cases. Add…
Nonparametric maximum likelihood approach to multiple change-point problems
Changliang Zou, Guosheng Yin, Long Feng +1
In multiple change-point problems, different data segments often follow different distributions, for which the changes may occur in the mean, scale or the entire distribution from…
Adaptive Test for Jump
Huifang Ma, Long Feng
We develop an adaptive jump test for discretely observed high-frequency semimartingales by combining the A"it-Sahalia--Jacod ratio statistic (A"it-Sahalia and Jacod, 2009) and the…
Time Series Generative Learning with Application to Brain Imaging Analysis
Zhenghao Li, Sanyou Wu, Long Feng
This paper focuses on the analysis of sequential image data, particularly brain imaging data such as MRI, fMRI, CT, with the motivation of understanding the brain aging process and…
Inverse Norm Weighted Maxsum Test for High Dimensional Location Parameters
Guowei Yan, Ping Zhao, Long Feng
In the context of high-dimensional data, we investigate the one-sample location testing problem. We introduce a max-type test based on the weighted spatial sign, which exhibits exc…
Max-sum tests for cross-sectional dependence of high-demensional panel data
Long Feng, Tiefeng Jiang, Binghui Liu +1
We consider a testing problem for cross-sectional dependence for high-dimensional panel data, where the number of cross-sectional units is potentially much larger than the number o…
Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment
Shuaida He, Liwen Chen, Long Feng
Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA under a federated learning se…
Sparse Kronecker Product Decomposition: A General Framework of Signal Region Detection in Image Regression
Sanyou Wu, Long Feng
This paper aims to present the first Frequentist framework on signal region detection in high-resolution and high-order image regression problems. Image data and scalar-on-image re…
Sorted Concave Penalized Regression
Long Feng, Cun-Hui Zhang
The Lasso is biased. Concave penalized least squares estimation (PLSE) takes advantage of signal strength to reduce this bias, leading to sharper error bounds in prediction, coeffi…
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…
Optimal Sign Test for High Dimensional Location Parameters
Long Feng
This article concerns tests for location parameters in cases where the data dimension is larger than the sample size. We propose a family of tests based on the optimality arguments…
Asymptotic Independence of the Sum and Maximum of Dependent Random Variables with Applications to High-Dimensional Tests
Long Feng, Tiefeng Jiang, Xiaoyun Li +1
For a set of dependent random variables, without stationary or the strong mixing assumptions, we derive the asymptotic independence between their sums and maxima. Then we apply thi…
Maximum-of-Differences Test for Comparing Multivariate K-Sample Distributions
Wei Lan, Long Feng, Runze Li +1
Comparing -sample distributions is a fundamental problem in data science that arises in a wide variety of fields and applications. In this article, we introduce a maximum-of-dif…
Spatial-Sign based Maxsum Test for High Dimensional Location Parameters
Jixuan Liu, Long Feng, Ping Zhao +1
In this study, we explore a robust testing procedure for the high-dimensional location parameters testing problem. Initially, we introduce a spatial-sign based max-type test statis…
Elliptical Regularized Hotelling Testing for High Dimensional Data
Long Feng, Le Zhou, Xiaoyi Wang
We consider one-sample testing of a high-dimensional location parameter under elliptically symmetric distributions with heavy tails and pervasive cross-sectional dependence. We pro…
Limit Law for the Maximum Interpoint Distance of High Dimensional Dependent Variables
Guowei Yan, Long Feng
In this paper, we considier the limiting distribution of the maximum interpoint Euclidean distance ,…
Adaptive Ridge-Regularized Hotelling Change-Point Tests for Functional Data
Ping Zhao, Long Feng
We propose a unified ridge-regularized Hotelling framework for detecting and locating mean changes in functional time series. A growing basis expansion converts the functional obse…
Robust Sparse Precision Matrix Estimation and its Application
Zhengke Lu, Long Feng
We address the problem of robust sparse estimation of the precision matrix for heavy-tailed distributions in high-dimensional settings. In such high-dimensional contexts, we observ…
Computationally efficient and data-adaptive changepoint inference in high dimension
Guanghui Wang, Long Feng
High-dimensional changepoint inference that adapts to various change patterns has received much attention recently. We propose a simple, fast yet effective approach for adaptive ch…
Spatial Sign based Principal Component Analysis for High Dimensional Data
Ping Zhao, Hongfei Wang, Long Feng
This article focuses on the robust principal component analysis (PCA) of high-dimensional data with elliptical distributions. We investigate the PCA of the sample spatial-sign cova…
Sparsity-Induced Global Matrix Autoregressive Model with Auxiliary Network Data
Sanyou Wu, Dan Yang, Yan Xu +1
Jointly modeling and forecasting economic and financial variables across a large set of countries has long been a significant challenge. Two primary approaches have been utilized t…
Projected Robust PCA with Application to Smooth Image Recovery
Long Feng, Junhui Wang
Most high-dimensional matrix recovery problems are studied under the assumption that the target matrix has certain intrinsic structures. For image data related matrix recovery prob…
Testing Independence Between High-Dimensional Random Vectors Using Rank-Based Max-Sum Tests
Hongfei Wang, Binghui Liu, Long Feng
In this paper, we address the problem of testing independence between two high-dimensional random vectors. Our approach involves a series of max-sum tests based on three well-known…
High Dimensional Bootstrap and Asymptotic Expansion for the -th Largest Coordinate
Long Feng
We study bootstrap inference for the th largest coordinate of a normalized sum of independent high-dimensional random vectors. Existing second-order theory for maxima does not d…
Fisher's combined probability test for cross-sectional independence in panel data models with serial correlation
Hongfei Wang, Binghui Liu, Long Feng +1
Testing cross-sectional independence in panel data models is of fundamental importance in econometric analysis with high-dimensional panels. Recently, econometricians began to turn…
A General Framework of Brain Region Detection And Genetic Variants Selection in Imaging Genetics
Siqiang Su, Zhenghao Li, Long Feng +1
Imaging genetics is a growing field that employs structural or functional neuroimaging techniques to study individuals with genetic risk variants potentially linked to specific ill…
Rank-Based Sparse Regression in Principal Components Space under Measurement Error
Long Feng, Xiaoyi Wang, Le Zhou
We study high-dimensional regression in principal components space when the predictors are observed with additive measurement error and the response errors may be heavy-tailed. The…
Spatial Sign based Direct Sparse Linear Discriminant Analysis for High Dimensional Data
Dan Zhuang, Long Feng
This paper investigates the robust linear discriminant analysis (LDA) problem with elliptical distributions in high-dimensional data. We propose a robust classification method, nam…
Power Comparison between High Dimensional t-Test, Sign, and Signed Rank Tests
Long Feng
In this paper, we propose a power comparison between high dimensional t-test, sign and signed rank test for the one sample mean test. We show that the high dimensional signed rank…
Deep Kronecker Network
Long Feng, Guang Yang
We propose Deep Kronecker Network (DKN), a novel framework designed for analyzing medical imaging data, such as MRI, fMRI, CT, etc. Medical imaging data is different from general i…
Scalar-Invariant Test for High-Dimensional Regression Coefficients
Long Feng
This article is concerned with simultaneous tests on linear regression coefficients in high-dimensional settings. When the dimensionality is larger than the sample size, the classi…
CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation
Yushi Feng, Junye Du, Qifan Wang +5
Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. However, this unrestricted action…
Tensor Elliptical Graphic Model
Jixuan Liu, Zhengke Lu, Le Zhou +2
We address the problem of robust estimation of sparse high dimensional tensor elliptical graphical model. Most of the research focus on tensor graphical model under normality. To e…
Functional Change Point Detection via Adjacent Deviation Subspace
Luoyao Yu, Long Feng, Xuehu Zhu
This paper develops the concept of the Adjacent Deviation Subspace (ADS), a novel framework for reducing infinite-dimensional functional data into finite-dimensional vector or scal…
Adaptive Strategy of Testing Alphas in High Dimensional Linear Factor Pricing Models
Chenxi Zhao, Ping Zhao, Long Feng +1
In recent years, there has been considerable research on testing alphas in high-dimensional linear factor pricing models. In our study, we introduce a novel max-type test procedure…
Spatial-Sign based High-Dimensional Location Test
Long Feng, Fasheng Sun
In this paper, we consider the problem of testing the mean vector in the high dimensional settings. We proposed a new robust scalar transform invariant test based on spatial sign.…
High-Dimensional Change Point Analysis for Temporally Dependent Data
Xiaoyi Wang, Le Zhou, Jixuan Liu +1
This paper develops adaptive procedures for detecting and locating mean changes in high-dimensional time series. Quadratic CUSUM statistics target dense changes, whereas coordinate…