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08/29/2023
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference
Despite the widespread utilization of Gaussian process models for versat...
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07/06/2023
On the Optimality of Functional Sliced Inverse Regression
In this paper, we prove that functional sliced inverse regression (FSIR)...
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07/04/2023
Monotone Cubic B-Splines
We present a method for fitting monotone curves using cubic B-splines wi...
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01/25/2022
Varying Coefficient Model via Adaptive Spline Fitting
The varying coefficient model has received wide attention from researche...
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12/16/2021
On Gibbs Sampling for Structured Bayesian Models Discussion of paper by Zanella and Roberts
This article is a discussion of Zanella and Roberts' paper: Multilevel l...
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11/30/2021
Convergence Rate of Multiple-try Metropolis Independent sampler
The Multiple-try Metropolis (MTM) method is an interesting extension of ...
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10/28/2021
Kernel-based Partial Permutation Test for Detecting Heterogeneous Functional Relationship
We propose a kernel-based partial permutation test for checking the equa...
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04/15/2021
Partition-Mallows Model and Its Inference for Rank Aggregation
Learning how to aggregate ranking lists has been an active research area...
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10/16/2020
Power of FDR Control Methods: The Impact of Ranking Algorithm, Tampered Design, and Symmetric Statistic
As the power of FDR control methods for high-dimensional variable select...
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10/13/2020
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks
Deep neural networks (DNNs) have become increasingly popular and achieve...
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07/15/2020
Measurement error models: from nonparametric methods to deep neural networks
The success of deep learning has inspired recent interests in applying n...
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07/02/2020
A Scale-free Approach for False Discovery Rate Control in Generalized Linear Models
The generalized linear models (GLM) have been widely used in practice to...
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06/01/2020
On Posterior Consistency of Bayesian Factor Models in High Dimensions
As a principled dimension reduction technique, factor models have been w...
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06/01/2020
Scalable Uncertainty Quantification via GenerativeBootstrap Sampler
It has been believed that the virtue of using statistical procedures is ...
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04/04/2020
Stratification and Optimal Resampling for Sequential Monte Carlo
Sequential Monte Carlo (SMC), also known as particle filters, has been w...
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02/20/2020
False Discovery Rate Control via Data Splitting
Selecting relevant features associated with a given response variable is...
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11/21/2019
Controlling False Discovery Rate Using Gaussian Mirrors
Simultaneously finding multiple influential variables and controlling th...
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11/06/2019
Minimax Nonparametric Two-sample Test
We consider the problem of comparing probability densities between two g...
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09/12/2019
Monte Carlo Approximation of Bayes Factors via Mixing with Surrogate Distributions
By mixing the posterior distribution with a surrogate distribution, of w...
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07/27/2019
The Wang-Landau Algorithm as Stochastic Optimization and its Acceleration
We show that the Wang-Landau algorithm can be formulated as a stochastic...
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05/28/2019
Generative Parameter Sampler For Scalable Uncertainty Quantification
Uncertainty quantification has been a core of the statistical machine le...
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10/05/2018
Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding
In this paper, we develop a low than character feature embedding called ...
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10/05/2018
IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms
By balancing margin-quantity maximization and margin-quality maximizatio...
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09/29/2018
Neuronized Priors for Bayesian Sparse Linear Regression
Although Bayesian variable selection procedures have been widely adopted...
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08/18/2018
Bayesian Hidden Markov Tree Models for Clustering Genes with Shared Evolutionary History
Determination of functions for poorly characterized genes is crucial for...
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07/04/2018
Randomization Inference for Peer Effects
Many previous causal inference studies require no interference among uni...
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05/04/2018
Global testing under the sparse alternatives for single index models
For the single index model y=f(β^τx,ϵ) with Gaussian design, and β is a...
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11/25/2015
L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
It is known that for a certain class of single index models (SIMs) Y = f...
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11/07/2015
Signed Support Recovery for Single Index Models in High-Dimensions
In this paper we study the support recovery problem for single index mod...
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10/30/2015
A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
We propose a new inferential framework for constructing confidence regio...
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06/08/2015
