Xin Ma,Suprateek Kundu;Alzheimer’s Disease Neuroimaging Initiative
Xin Ma
Recent medical imaging studies have given rise to distinct but inter-related datasets corresponding to multiple experimental tasks or longitudinal visits. Standard scalar-on-image regression models that fit each dataset separately are not e...
Nonparametric two-sample tests of high dimensional mean vectors via random integration [0.03%]
高维均值向量的非参数双样本检验及其随机积分方法研究
Yunlu Jiang,Xueqin Wang,Canhong Wen et al.
Yunlu Jiang et al.
Testing the equality of the means in two samples is a fundamental statistical inferential problem. Most of the existing methods are based on the sum-of-squares or supremum statistics. They are possibly powerful in some situations, but not i...
An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices [0.03%]
基于对称正定矩阵流形的收缩估计的经验贝叶斯方法
Chun-Hao Yang,Hani Doss,Baba C Vemuri
Chun-Hao Yang
The James-Stein estimator is an estimator of the multivariate normal mean and dominates the maximum likelihood estimator (MLE) under squared error loss. The original work inspired great interest in developing shrinkage estimators for a vari...
Ye Tian,Yang Feng
Ye Tian
In this work, we study the transfer learning problem under highdimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we p...
Prior-Preconditioned Conjugate Gradient Method for Accelerated Gibbs Sampling in "Large n, Large p" Bayesian Sparse Regression [0.03%]
一种加速Gibbs抽样的预条件共轭梯度方法及其在“大n，大p”贝叶斯稀疏回归中的应用
Akihiko Nishimura,Marc A Suchard
Akihiko Nishimura
In a modern observational study based on healthcare databases, the number of observations and of predictors typically range in the order of 105-106 and of 104-105. Despite the large sample size, data rarely provide sufficient information to...
Understanding Implicit Regularization in Over-Parameterized Single Index Model [0.03%]
过度参数化单指标模型中隐式正则化的理解
Jianqing Fan,Zhuoran Yang,Mengxin Yu
Jianqing Fan
In this paper, we leverage over-parameterization to design regularization-free algorithms for the high-dimensional single index model and provide theoretical guarantees for the induced implicit regularization phenomenon. Specifically, we st...
Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process [0.03%]
基于门槛高斯过程的贝叶斯空间盲源分离方法
Ben Wu,Ying Guo,Jian Kang
Ben Wu
Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the...
Bias-adjusted spectral clustering in multi-layer stochastic block models [0.03%]
偏倚调整的多层随机块模型谱聚类方法研究
Jing Lei,Kevin Z Lin
Jing Lei
We consider the problem of estimating common community structures in multi-layer stochastic block models, where each single layer may not have sufficient signal strength to recover the full community structure. In order to efficiently aggre...
Xiao Wu,Fabrizia Mealli,Marianthi-Anna Kioumourtzoglou et al.
Xiao Wu et al.
In the context of a binary treatment, matching is a well-established approach in causal inference. However, in the context of a continuous treatment or exposure, matching is still underdeveloped. We propose an innovative matching approach t...
Keyur H Desai,John D Storey
Keyur H Desai
A growing number of modern scientific problems in areas such as genomics, neurobiology, and spatial epidemiology involve the measurement and analysis of thousands of related features that may be stochastically dependent at arbitrarily stron...