A study of the impact of COVID-19 on the Chinese stock market based on a new textual multiple ARMA model [0.03%]
基于新型文本多重ARMA模型的COVID-19对中国股市影响研究
Weijun Xu,Zhineng Fu,Hongyi Li et al.
Weijun Xu et al.
Coronavirus 2019 (COVID-19) has caused violent fluctuation in stock markets, and led to heated discussion in stock forums. The rise and fall of any specific stock is influenced by many other stocks and emotions expressed in forum discussion...
Sample Selection Bias in Evaluation of Prediction Performance of Causal Models [0.03%]
因果模型预测性能评估中的样本选择偏差
James P Long,Min Jin Ha
James P Long
Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction perfor...
Junghi Kim,Hongtu Zhu,Xiao Wang et al.
Junghi Kim et al.
With the advent of high-throughput sequencing, an efficient computing strategy is required to deal with large genomic data sets. The challenge of estimating a large precision matrix has garnered substantial research attention for its direct...
Fused Lasso Regression for Identifying Differential Correlations in Brain Connectome Graphs [0.03%]
融合套索回归在脑连接组差异相关性识别中的应用
Donghyeon Yu,Sang Han Lee,Johan Lim et al.
Donghyeon Yu et al.
In this paper, we propose a procedure to find differential edges between two graphs from high-dimensional data. We estimate two matrices of partial correlations and their differences by solving a penalized regression problem. We assume spar...
Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease [0.03%]
基于电子健康档案的生存分析：慢性肾病实验研究
Yolanda Hagar,David Albers,Rimma Pivovarov et al.
Yolanda Hagar et al.
This paper presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the EHR data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York...
Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments [0.03%]
在普通计算环境下用于大规模空间数据的实用贝叶斯建模与推理方法研究
Lu Zhang,Abhirup Datta,Sudipto Banerjee
Lu Zhang
With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has generated substantial interest over the last decade, already...
Unsupervised random forests [0.03%]
无监督随机森林
Alejandro Mantero,Hemant Ishwaran
Alejandro Mantero
sidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the stagg...
A framework for stability-based module detection in correlation graphs [0.03%]
基于稳定性的模块检测算法及其在相关图中的应用框架
Mingmei Tian,Rachael Hageman Blair,Lina Mu et al.
Mingmei Tian et al.
Graphs can be used to represent the direct and indirect relationships between variables, and elucidate complex relationships and interdependencies. Detecting structure within a graph is a challenging problem. This problem is studied over a ...
A clustering method for graphical handwriting components and statistical writership analysis [0.03%]
一种图形手写组件的聚类方法及统计书写人识别方法
Amy M Crawford,Nicholas S Berry,Alicia L Carriquiry
Amy M Crawford
Handwritten documents can be characterized by their content or by the shape of the written characters. We focus on the problem of comparing a person's handwriting to a document of unknown provenance using the shape of the writing, as is don...
Knot selection in sparse Gaussian processes with a variational objective function [0.03%]
稀疏高斯过程中具有变分目标函数的结点选择
Nathaniel Garton,Jarad Niemi,Alicia Carriquiry
Nathaniel Garton
Sparse, knot-based Gaussian processes have enjoyed considerable success as scalable approximations of full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots...