
Rank Determination in Tensor Factor Model
Factor model is an appealing and effective analytic tool for highdimens...
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Adaptive Estimation In HighDimensional Additive Models With MultiResolution Group Lasso
In additive models with many nonparametric components, a number of regul...
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Tensor Factor Model Estimation by Iterative Projection
Tensor time series, which is a time series consisting of tensorial obser...
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Confidence intervals for multiple isotonic regression and other monotone models
We consider the problem of constructing pointwise confidence intervals i...
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Second order Poincaré inequalities and debiasing arbitrary convex regularizers when p/n → γ
A new Central Limit Theorem (CLT) is developed for random variables of t...
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Local Inference in Additive Models with Decorrelated Local Linear Estimator
Additive models, as a natural generalization of linear regression, have ...
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Limit distribution theory for multiple isotonic regression
We study limit distributions for the tuningfree maxmin block estimator...
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Factor Models for HighDimensional Tensor Time Series
Large tensor (multidimensional array) data are now routinely collected ...
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Extreme Nonlinear Correlation for Multiple Random Variables and Stochastic Processes with Applications to Additive Models
The maximum correlation of functions of a pair of random variables is an...
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DeBiasing The Lasso With DegreesofFreedom Adjustment
This paper studies schemes to debias the Lasso in sparse linear regress...
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Isotonic Regression in MultiDimensional Spaces and Graphs
In this paper we study minimax and adaptation rates in general isotonic ...
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Second order Stein: SURE for SURE and other applications in highdimensional inference
Stein's formula states that a random variable of the form z^ f(z)  div ...
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Sorted Concave Penalized Regression
The Lasso is biased. Concave penalized least squares estimation (PLSE) t...
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Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries
In this article, we develop methods for estimating a low rank tensor fro...
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Incoherent Tensor Norms and Their Applications in Higher Order Tensor Completion
In this paper, we investigate the sample size requirement for a general ...
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On Tensor Completion via Nuclear Norm Minimization
Many problems can be formulated as recovering a lowrank tensor. Althoug...
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Asymptotic normality and optimalities in estimation of large Gaussian graphical models
The Gaussian graphical model, a popular paradigm for studying relationsh...
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Exact Sparse Recovery with L0 Projections
Many applications concern sparse signals, for example, detecting anomali...
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Calibrated Elastic Regularization in Matrix Completion
This paper concerns the problem of matrix completion, which is to estima...
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One Permutation Hashing for Efficient Search and Learning
Recently, the method of bbit minwise hashing has been applied to large...
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Optimality of Graphlet Screening in High Dimensional Variable Selection
Consider a linear regression model where the design matrix X has n rows ...
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Sparse Matrix Inversion with Scaled Lasso
We propose a new method of learning a sparse nonnegativedefinite target...
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A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems
This paper develops a general theoretical framework to analyze structure...
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Estimation And Selection Via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications
The ℓ_1penalized method, or the Lasso, has emerged as an important tool...
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A General Theory of Concave Regularization for High Dimensional Sparse Estimation Problems
Concave regularization methods provide natural procedures for sparse rec...
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Scaled Sparse Linear Regression
Scaled sparse linear regression jointly estimates the regression coeffic...
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CunHui Zhang
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Department of Statistics and Biostatisticsat Rutgers University