Optimal distributed subsampling for accelerated failure time models with massive censored data [0.03%]
巨量删失数据下加速失效时间模型的最优分布式抽样方法
Chunjie Wang,Jing Li,Xiaohui Yuan
Chunjie Wang
The availability of massive data stored across multiple locations is increasing in many fields. The data at each site often exhibits large-scale features. Current research primarily focuses on such datasets that consist of uncensored observ...
IMCKDE algorithm: an improvement in a clustering technique based on kernel density estimation [0.03%]
基于核密度估计的聚类技术的改进算法IMCKDE
Paulo Muraro Ferreira,Mariana Kleina
Paulo Muraro Ferreira
Given the increasing volume of data available, much of which lacks established categories, the development of algorithms capable of finding patterns in raw, unclassified data is becoming increasingly important. One type of clustering algori...
A review and comparison of methods of testing for heteroskedasticity in the linear regression model [0.03%]
线性回归模型中异方差性检验方法的述评与比较
Thomas Farrar,Renette Blignaut,Retha Luus et al.
Thomas Farrar et al.
This study reviews inferential methods for diagnosing heteroskedasticity in the linear regression model, classifying the methods into four types: deflator tests, auxiliary design tests, omnibus tests, and portmanteau tests. A Monte Carlo si...
Inconsistency of three indices in measuring the association between the risk factor and the risk of a disease [0.03%]
三个指标在测量危险因素与疾病风险关联中的不一致性
Changyong Feng,Hongyue Wang,Honghong Liu
Changyong Feng
The relative risk (r), risk difference (d), and odds ratio ( θ ) are three commonly used indices in epidemiology to quantify the association between the risk of a disease and the exposure to a risk factor. However, it has been reported...
A review and comparison of methods of parameter estimation and inference for heteroskedastic linear regression models [0.03%]
异方差线性模型参数估计和推断方法的述评及比较研究
Thomas Farrar,Renette Blignaut,Retha Luus et al.
Thomas Farrar et al.
This article reviews methods of parameter estimation and inference in the linear regression model under heteroskedasticity. Several approaches to feasible weighted least squares estimation of the parameter vector are reviewed, along with va...
Multiresolution granger causality testing with variational mode decomposition: a python software [0.03%]
基于变分模分解的多分辨率格兰杰因果检验：一款Python软件包
Foued Saâdaoui,Hana Rabbouch
Foued Saâdaoui
In this paper, we introduce a novel and advanced multiscale approach to Granger causality testing, achieved by integrating Variational Mode Decomposition (VMD) with traditional statistical causality methods. Our approach decomposes complex ...
Estimating longitudinal biomarker effects using a Lasso-network constrained time-Varying mixed effects model [0.03%]
基于Lasso-网络约束的时间变化混合效应模型估算纵向生物标志物效应
Shiqi Liu,Weiwei Zhuang,Jinfeng Xu et al.
Shiqi Liu et al.
The relationship between covariates and outcomes can change over time, regardless of whether these covariates are time-varying or static. For instance, the influence of circulating biomarkers like white blood cell counts on the efficacy of ...
C Satheesh Kumar,Prince Sathyan
C Satheesh Kumar
Here we consider a weighted version of the negative binomial distribution and illustrate its usefulness through fitting Covid-19 datasets. We obtain several important properties of the distribution such as probability generating function, c...
Generalised random tessellation stratified sampling over auxiliary spaces [0.03%]
基于辅助空间的广义随机镶嵌分层抽样方法
B L Robertson,C J Price,M Reale et al.
B L Robertson et al.
Generalised Random Tessellation Stratified (GRTS) is a popular spatially balanced sampling design. GRTS can draw spatially balanced probability samples in two dimensions but has not been used to sample higher-dimensional auxiliary spaces. T...
Lasso Monte Carlo, a variation on multi fidelity methods for high-dimensional uncertainty quantification [0.03%]
一种用于高维度不确定性量化的方法-lasso蒙特卡洛方法及其多 fidelity变体方法的研究
Arnau Albà,Romana Boiger,Dimitri Rochman et al.
Arnau Albà et al.
Uncertainty quantification (UQ) is an active area of research, and an essential technique used in all fields of science and engineering. The most common methods for UQ are Monte Carlo and surrogate-modelling. The former method is dimensiona...