
Adaptive Robust Large Volatility Matrix Estimation Based on HighFrequency Financial Data
Several novel statistical methods have been developed to estimate large ...
read it

Bridging factor and sparse models
Factor and sparse models are two widely used methods to impose a lowdim...
read it

The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID19 Cases
With the severity of the COVID19 outbreak, we characterize the nature o...
read it

Spectral Methods for Data Science: A Statistical Perspective
Spectral methods have emerged as a simple yet surprisingly effective app...
read it

Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction
The measurement of treatment (intervention) effects on a single (or just...
read it

Recent Developments on Factor Models and its Applications in Econometric Learning
This paper makes a selective survey on the recent development of the fac...
read it

Convex and Nonconvex Optimization Are Both MinimaxOptimal for Noisy Blind Deconvolution
We investigate the effectiveness of convex relaxation and nonconvex opti...
read it

Canonical thresholding for nonsparse highdimensional linear regression
We consider a highdimensional linear regression problem. Unlike many pa...
read it

Understanding Implicit Regularization in OverParameterized Nonlinear Statistical Model
We study the implicit regularization phenomenon induced by simple optimi...
read it

Community Network AutoRegression for HighDimensional Time Series
Modeling responses on the nodes of a largescale network is an important...
read it

Semiparametric Tensor Factor Analysis by Iteratively Projected SVD
This paper introduces a general framework of Semiparametric TEnsor FActo...
read it

When is best subset selection the "best"?
Best subset selection (BSS) is fundamental in statistics and machine lea...
read it

An ℓ_p theory of PCA and spectral clustering
Principal Component Analysis (PCA) is a powerful tool in statistics and ...
read it

Bootstrapping ℓ_pStatistics in High Dimensions
This paper considers a new bootstrap procedure to estimate the distribut...
read it

Hypothesis testing for eigenspaces of covariance matrix
Eigenspaces of covariance matrices play an important role in statistical...
read it

Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data
This paper delivers improved theoretical guarantees for the convex progr...
read it

Statistical Inference for HighDimensional MatrixVariate Factor Model
This paper considers the estimation and inference of factor loadings, la...
read it

SIMPLE: Statistical Inference on Membership Profiles in Large Networks
Network data is prevalent in many contemporary big data applications in ...
read it

Estimating Number of Factors by Adjusted Eigenvalues Thresholding
Determining the number of common factors is an important and practical t...
read it

Optimal estimation of functionals of highdimensional mean and covariance matrix
Motivated by portfolio allocation and linear discriminant analysis, we c...
read it

Learning Latent Factors from Diversified Projections and its Applications to OverEstimated and Weak Factors
Estimations and applications of factor models often rely on the crucial ...
read it

CommunicationEfficient Accurate Statistical Estimation
When the data are stored in a distributed manner, direct application of ...
read it

Inference and Uncertainty Quantification for Noisy Matrix Completion
Noisy matrix completion aims at estimating a lowrank matrix given only ...
read it

LowRank Principal Eigenmatrix Analysis
Sparse PCA is a widely used technique for highdimensional data analysis...
read it

Adaptive Huber Regression on Markovdependent Data
Highdimensional linear regression has been intensively studied in the c...
read it

A Selective Overview of Deep Learning
Deep learning has arguably achieved tremendous success in recent years. ...
read it

Bayesian Factoradjusted Sparse Regression
This paper investigates the highdimensional linear regression with high...
read it

Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
This paper studies noisy lowrank matrix completion: given partial and c...
read it

Asymptotic Theory of Eigenvectors for Large Random Matrices
Characterizing the exact asymptotic distributions of highdimensional ei...
read it

Higher Moment Estimation for Ellipticallydistributed Data: Is it Necessary to Use a Sledgehammer to Crack an Egg?
Multivariate ellipticallycontoured distributions are widely used for mo...
read it

Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed LowRank Matrices
This paper is concerned with a curious phenomenon in spectral estimation...
read it

Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
We study the fundamental tradeoffs between statistical accuracy and comp...
read it

Robust high dimensional factor models with applications to statistical machine learning
Factor models are a class of powerful statistical models that have been ...
read it

Tensor Methods for Additive Index Models under Discordance and Heterogeneity
Motivated by the sampling problems and heterogeneity issues common in hi...
read it

Bernstein's inequality for general Markov chains
We prove a sharp Bernstein inequality for generalstatespace and not ne...
read it

Optimal Subspace Estimation Using Overidentifying Vectors via Generalized Method of Moments
Many statistical models seek relationship between variables via subspace...
read it

Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
This paper considers the problem of solving systems of quadratic equatio...
read it

Testability of highdimensional linear models with nonsparse structures
This paper studies hypothesis testing and confidence interval constructi...
read it

Hoeffding's lemma for Markov Chains and its applications to statistical learning
We establish the counterpart of Hoeffding's lemma for Markov dependent r...
read it

Principal component analysis for big data
Big data is transforming our world, revolutionizing operations and analy...
read it

FARMTest: FactorAdjusted Robust Multiple Testing with False Discovery Control
Largescale multiple testing with correlated and heavytailed data arise...
read it

A New Perspective on Robust MEstimation: Finite Sample Theory and Applications to DependenceAdjusted Multiple Testing
Heavytailed errors impair the accuracy of the least squares estimate, w...
read it

Spectral Method and Regularized MLE Are Both Optimal for TopK Ranking
This paper is concerned with the problem of topK ranking from pairwise ...
read it

Sufficient Forecasting Using Factor Models
We consider forecasting a single time series when there is a large numbe...
read it

A Projection Based Conditional Dependence Measure with Applications to Highdimensional Undirected Graphical Models
Measuring conditional dependence is an important topic in statistics wit...
read it

High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Graphical models are commonly used tools for modeling multivariate rando...
read it

Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification
We propose a high dimensional classification method that involves nonpar...
read it

Challenges of Big Data Analysis
Big Data bring new opportunities to modern society and challenges to dat...
read it

Strong oracle optimality of folded concave penalized estimation
Folded concave penalization methods have been shown to enjoy the strong ...
read it

A ROAD to Classification in High Dimensional Space
For highdimensional classification, it is well known that naively perfo...
read it