Bayesian Inference Using the Proximal Mapping: Uncertainty Quantification Under Varying Dimensionality [0.03%]
基于邻近映射的贝叶斯推断：不同维度下的不确定性量化
Maoran Xu,Hua Zhou,Yujie Hu et al.
Maoran Xu et al.
In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recovery under an unknown low rank, etc. Despite the ease of obta...
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models [0.03%]
NEYMAN-SCOTT过程的时空聚类及其与贝叶斯非参数混合模型的关系
Yixin Wang,Anthony Degleris,Alex Williams et al.
Yixin Wang et al.
Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property i...
Stephen Bates,Trevor Hastie,Robert Tibshirani
Stephen Bates
Cross-validation is a widely-used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, fit...
Estimation of Linear Functionals in High-Dimensional Linear Models: From Sparsity to Nonsparsity [0.03%]
高维线性模型中线性泛函的估计：从稀疏到非稀疏
Junlong Zhao,Yang Zhou,Yufeng Liu
Junlong Zhao
High dimensional linear models are commonly used in practice. In many applications, one is interested in linear transformations β ⊤ x of regression coefficients β ∈ R p , where x is a specific point and is not requ...
Cong Zhang,Tejasv Bedi,Chul Moon et al.
Cong Zhang et al.
Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure ...
Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer's disease [0.03%]
利用批量基因表达数据估计细胞类型特异性基因共表达网络及其在阿尔茨海默病中的应用
Chang Su,Jingfei Zhang,Hongyu Zhao
Chang Su
Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tiss...
Jianqing Fan,Zhipeng Lou,Mengxin Yu
Jianqing Fan
We propose the Factor Augmented (sparse linear) Regression Model (FARM) that not only admits both the latent factor regression and sparse linear regression as special cases but also bridges dimension reduction and sparse regression together...
Estimating trans-ancestry genetic correlation with unbalanced data resources [0.03%]
利用不平衡的数据资源估计跨祖先遗传关系系数
Bingxin Zhao,Xiaochen Yang,Hongtu Zhu
Bingxin Zhao
The aim of this paper is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically-predicted observations. These correlations describe how genetic architecture o...
Statistical Inferences for Complex Dependence of Multimodal Imaging Data [0.03%]
多模态影像数据复杂相关性的统计推断方法研究
Jinyuan Chang,Jing He,Jian Kang et al.
Jinyuan Chang et al.
Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this paper, we propose rigorous statistical testing procedures f...
Operator-induced structural variable selection for identifying materials genes [0.03%]
基于操作符的结构型变量选择方法寻找材料基因
Shengbin Ye,Thomas P Senftle,Meng Li
Shengbin Ye
In the emerging field of materials informatics, a fundamental task is to identify physicochemically meaningful descriptors, or materials genes, which are engineered from primary features and a set of elementary algebraic operators through c...