Wei Hu,Weining Shen,Hua Zhou et al.
Wei Hu et al.
We propose a novel linear discriminant analysis (LDA) approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence of the conventional LDA and the ordinary le...
Bayesian State Space Modeling of Physical Processes in Industrial Hygiene [0.03%]
工业卫生中物理过程的贝叶斯状态空间建模方法研究
Nada Abdalla,Sudipto Banerjee,Gurumurthy Ramachandran et al.
Nada Abdalla et al.
Exposure assessment models are deterministic models derived from physical-chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly measured. In addition, inference on important parameters su...
Bayesian Modeling for Physical Processes in Industrial Hygiene Using Misaligned Workplace Data [0.03%]
基于工作场所数据的贝叶斯模型在工业卫生中的物理过程建模方法研究
João V D Monteiro,Sudipto Banerjee,Gurumurthy Ramachandran
João V D Monteiro
In industrial hygiene, a worker's exposure to chemical, physical, and biological agents is increasingly being modeled using deterministic physical models that study exposures near and farther away from a contaminant source. However, predict...
An-Ting Jhuang,Montserrat Fuentes,Jacob L Jones et al.
An-Ting Jhuang et al.
Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to refl...
Wenhao Hu,Eric B Laber,Clay Barker et al.
Wenhao Hu et al.
Penalized regression methods that perform simultaneous model selection and estimation are ubiquitous in statistical modeling. The use of such methods is often unavoidable as manual inspection of all possible models quickly becomes intractab...
Zhengling Qi,Yufeng Liu
Zhengling Qi
Classification problems are commonly seen in practice. In this paper, we aim to develop classifiers that can enjoy great interpretability as linear classifiers, and at the same time have model flexibility as nonlinear classifiers. We propos...
Permutation and Grouping Methods for Sharpening Gaussian Process Approximations [0.03%]
用于改进高斯过程逼近的排列与分组方法
Joseph Guinness
Joseph Guinness
Vecchia's approximate likelihood for Gaussian process parameters depends on how the observations are ordered, which has been cited as a deficiency. This article takes the alternative standpoint that the ordering can be tuned to sharpen the ...
Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets [0.03%]
元克里金法：大型空间数据集的可扩展贝叶斯建模和推理
Rajarshi Guhaniyogi,Sudipto Banerjee
Rajarshi Guhaniyogi
Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In t...
A Geometric Approach to Archetypal Analysis and Nonnegative Matrix Factorization [0.03%]
基于几何的典型分析与非负矩阵分解方法研究
Anil Damle,Yuekai Sun
Anil Damle
Archetypal analysis and non-negative matrix factorization (NMF) are staples in a statisticians toolbox for dimension reduction and exploratory data analysis. We describe a geometric approach to both NMF and archetypal analysis by interpreti...