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07/21/2022
Bayesian Sparse Gaussian Mixture Model in High Dimensions
We establish the minimax risk for parameter estimation in sparse high-di...
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07/04/2022
Statistical inference of random graphs with a surrogate likelihood function
Spectral estimators have been broadly applied to statistical network ana...
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03/30/2022
Eigenvector-Assisted Statistical Inference for Signal-Plus-Noise Matrix Models
In this paper, we develop a generalized Bayesian inference framework for...
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06/17/2021
Entrywise limit theorems of eigenvectors and their one-step refinement for sparse random graphs
We establish finite-sample Berry-Esseen theorems for the entrywise limit...
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03/07/2021
Euclidean Representation of Low-Rank Matrices and Its Statistical Applications
Low-rank matrices are pervasive throughout statistics, machine learning,...
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10/10/2019
Efficient Estimation for Random Dot Product Graphs via a One-step Procedure
We propose a one-step procedure to efficiently estimate the latent posit...
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04/26/2019
Optimal Bayesian Estimation for Random Dot Product Graphs
We propose a Bayesian approach, called the posterior spectral embedding,...
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02/15/2019
BAREB: A Bayesian repulsive biclustering model for periodontal data
Preventing periodontal diseases (PD) and maintaining the structure and f...
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08/22/2018
Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions
We propose a Bayesian methodology for estimating spiked covariance matri...
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07/10/2018
A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration
The Gaussian stochastic process (GaSP) is a useful technique for predict...
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03/03/2018
Bayesian Projected Calibration of Computer Models
We develop a Bayesian approach called Bayesian projected calibration to ...
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12/15/2017
A Theoretical Framework for Bayesian Nonparametric Regression: Orthonormal Random Series and Rates of Contraction
We develop a unifying framework for Bayesian nonparametric regression to...
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10/22/2017