:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Tang, Tao, Wu, Nan, Cheng, Xiuyuan, Dunson, David
Format:	Preprint
Published:	2024
Subjects:	Statistics Theory
Online Access:	https://arxiv.org/abs/2407.09286
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021)

Learning manifold diffusion semigroups from graph transition matrices
by: Cheng, Xiuyuan, et al.
Published: (2026)

Improved convergence rate of kNN graph Laplacians: differentiable self-tuned affinity
by: Cheng, Xiuyuan, et al.
Published: (2024)

Bayesian nonparametric modeling of latent partitions via Stirling-gamma priors
by: Zito, Alessandro, et al.
Published: (2023)

Kernel Two-Sample Tests for Manifold Data
by: Cheng, Xiuyuan, et al.
Published: (2021)

Variational bagging: a robust approach for Bayesian uncertainty quantification
by: Fan, Shitao, et al.
Published: (2025)

Using prior information to boost power in correlation structure support recovery
by: Ding, Ziyang, et al.
Published: (2021)

Flow-based generative models as iterative algorithms in probability space
by: Xie, Yao, et al.
Published: (2025)

Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
by: Cheng, Xiuyuan, et al.
Published: (2022)

Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes
by: Zhu, Yichen, et al.
Published: (2022)

Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis
by: Chandra, Noirrit Kiran, et al.
Published: (2023)

Point processes with event time uncertainty
by: Cheng, Xiuyuan, et al.
Published: (2024)

Deep spatio-temporal point processes: Advances and new directions
by: Cheng, Xiuyuan, et al.
Published: (2025)

On the Statistical Capacity of Deep Generative Models
by: Tam, Edric, et al.
Published: (2025)

Direct Bayesian Regression for Distribution-valued Covariates
by: Tang, Bohao, et al.
Published: (2023)

Sparse Data-Driven Random Projection in Regression for High-Dimensional Data
by: Parzer, Roman, et al.
Published: (2023)

Robustifying likelihoods by optimistically re-weighting data
by: Dewaskar, Miheer, et al.
Published: (2023)

Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates
by: Ojha, Abhishek, et al.
Published: (2024)

Marginally Interpretable Spatial Logistic Regression With Bridge Processes
by: Changwoo J. Lee, et al.
Published: (2026)

The High-Dimensional Asymptotics of Principal Component Regression
by: Green, Alden, et al.
Published: (2024)

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

Covariance Regression with High-Dimensional Predictors
by: He, Yuheng, et al.
Published: (2024)

Asymptotic Distribution-Free Tests for Ultra-high Dimensional Parametric Regressions via Projected Empirical Processes and $p$-value Combination
by: Tan, Falong, et al.
Published: (2026)

Functional Adaptive Huber Linear Regression
by: Peng, Ling, et al.
Published: (2024)

A Hybrid Prior Bayesian Method for Combining Domestic Real-World Data and Overseas Data in Global Drug Development
by: Chen, Keer, et al.
Published: (2025)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)

Adaptive Geometric Regression for High-Dimensional Structured Data
by: Gajer, Pawel, et al.
Published: (2025)

Adaptive Penalized Doubly Robust Regression for Longitudinal Data
by: Wang, Yuyao, et al.
Published: (2026)

Bayesian Quantile Estimation and Regression with Martingale Posteriors
by: Fong, Edwin, et al.
Published: (2024)

Curse of Dimensionality in Bayesian Model Updating
by: Binbin, Li, et al.
Published: (2025)

High Dimensional Logistic Regression Under Network Dependence
by: Mukherjee, Somabha, et al.
Published: (2021)

Joint Mean and Correlation Regression Models for Multivariate Data
by: Tho, Zhi Yang, et al.
Published: (2024)

Bayesian Sphere-on-Sphere Regression with Optimal Transport Maps
by: Ng, Tin Lok James, et al.
Published: (2025)

On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
by: Chakraborty, Saptarshi, et al.
Published: (2024)

A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

An Ising Similarity Regression Model for Modeling Multivariate Binary Data
by: Tho, Zhi Yang, et al.
Published: (2024)

Statistical Inference on High Dimensional Gaussian Graphical Regression Models
by: Meng, Xuran, et al.
Published: (2024)

Minimax Optimal Rates for Regression on Manifolds and Distributions
by: Tang, Rong, et al.
Published: (2025)

Functional Adaptive Double-Sparsity Estimator for Functional Linear Regression Model with Multiple Functional Covariates
by: Cao, Cheng, et al.
Published: (2023)

Bayesian High-dimensional Linear Regression with Sparse Projection-posterior
by: Pal, Samhita, et al.
Published: (2024)