
Inference for Heteroskedastic PCA with Missing Data
This paper studies how to construct confidence regions for principal com...
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A provable twostage algorithm for penalized hazards regression
From an optimizer's perspective, achieving the global optimum for a gene...
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SampleEfficient Reinforcement Learning for LinearlyParameterized MDPs with a Generative Model
The curse of dimensionality is a widely known issue in reinforcement lea...
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Adaptive Robust Large Volatility Matrix Estimation Based on HighFrequency Financial Data
Several novel statistical methods have been developed to estimate large ...
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Bridging factor and sparse models
Factor and sparse models are two widely used methods to impose a lowdim...
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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...
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Spectral Methods for Data Science: A Statistical Perspective
Spectral methods have emerged as a simple yet surprisingly effective app...
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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...
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Recent Developments on Factor Models and its Applications in Econometric Learning
This paper makes a selective survey on the recent development of the fac...
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Convex and Nonconvex Optimization Are Both MinimaxOptimal for Noisy Blind Deconvolution
We investigate the effectiveness of convex relaxation and nonconvex opti...
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Canonical thresholding for nonsparse highdimensional linear regression
We consider a highdimensional linear regression problem. Unlike many pa...
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Understanding Implicit Regularization in OverParameterized Nonlinear Statistical Model
We study the implicit regularization phenomenon induced by simple optimi...
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Community Network AutoRegression for HighDimensional Time Series
Modeling responses on the nodes of a largescale network is an important...
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Semiparametric Tensor Factor Analysis by Iteratively Projected SVD
This paper introduces a general framework of Semiparametric TEnsor FActo...
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When is best subset selection the "best"?
Best subset selection (BSS) is fundamental in statistics and machine lea...
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An ℓ_p theory of PCA and spectral clustering
Principal Component Analysis (PCA) is a powerful tool in statistics and ...
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Bootstrapping ℓ_pStatistics in High Dimensions
This paper considers a new bootstrap procedure to estimate the distribut...
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Hypothesis testing for eigenspaces of covariance matrix
Eigenspaces of covariance matrices play an important role in statistical...
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Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data
This paper delivers improved theoretical guarantees for the convex progr...
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Statistical Inference for HighDimensional MatrixVariate Factor Model
This paper considers the estimation and inference of factor loadings, la...
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SIMPLE: Statistical Inference on Membership Profiles in Large Networks
Network data is prevalent in many contemporary big data applications in ...
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Estimating Number of Factors by Adjusted Eigenvalues Thresholding
Determining the number of common factors is an important and practical t...
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Optimal estimation of functionals of highdimensional mean and covariance matrix
Motivated by portfolio allocation and linear discriminant analysis, we c...
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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 ...
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CommunicationEfficient Accurate Statistical Estimation
When the data are stored in a distributed manner, direct application of ...
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Inference and Uncertainty Quantification for Noisy Matrix Completion
Noisy matrix completion aims at estimating a lowrank matrix given only ...
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LowRank Principal Eigenmatrix Analysis
Sparse PCA is a widely used technique for highdimensional data analysis...
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Adaptive Huber Regression on Markovdependent Data
Highdimensional linear regression has been intensively studied in the c...
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A Selective Overview of Deep Learning
Deep learning has arguably achieved tremendous success in recent years. ...
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Bayesian Factoradjusted Sparse Regression
This paper investigates the highdimensional linear regression with high...
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Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
This paper studies noisy lowrank matrix completion: given partial and c...
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Asymptotic Theory of Eigenvectors for Large Random Matrices
Characterizing the exact asymptotic distributions of highdimensional ei...
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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...
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Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed LowRank Matrices
This paper is concerned with a curious phenomenon in spectral estimation...
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Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
We study the fundamental tradeoffs between statistical accuracy and comp...
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Robust high dimensional factor models with applications to statistical machine learning
Factor models are a class of powerful statistical models that have been ...
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Tensor Methods for Additive Index Models under Discordance and Heterogeneity
Motivated by the sampling problems and heterogeneity issues common in hi...
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Bernstein's inequality for general Markov chains
We prove a sharp Bernstein inequality for generalstatespace and not ne...
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Optimal Subspace Estimation Using Overidentifying Vectors via Generalized Method of Moments
Many statistical models seek relationship between variables via subspace...
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Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
This paper considers the problem of solving systems of quadratic equatio...
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Testability of highdimensional linear models with nonsparse structures
This paper studies hypothesis testing and confidence interval constructi...
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Hoeffding's lemma for Markov Chains and its applications to statistical learning
We establish the counterpart of Hoeffding's lemma for Markov dependent r...
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Principal component analysis for big data
Big data is transforming our world, revolutionizing operations and analy...
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FARMTest: FactorAdjusted Robust Multiple Testing with False Discovery Control
Largescale multiple testing with correlated and heavytailed data arise...
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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...
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Spectral Method and Regularized MLE Are Both Optimal for TopK Ranking
This paper is concerned with the problem of topK ranking from pairwise ...
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Sufficient Forecasting Using Factor Models
We consider forecasting a single time series when there is a large numbe...
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A Projection Based Conditional Dependence Measure with Applications to Highdimensional Undirected Graphical Models
Measuring conditional dependence is an important topic in statistics wit...
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High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Graphical models are commonly used tools for modeling multivariate rando...
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Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification
We propose a high dimensional classification method that involves nonpar...
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