Threshold Knot Selection for Large-Scale Spatial Models With Applications to the Deepwater Horizon Disaster [0.03%]
深水地平线灾难事件下的大规模空间数据分析中的结点选择问题研究
Casey M Jelsema,Richard K Kwok,Shyamal D Peddada
Casey M Jelsema
Large spatial datasets are typically modeled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating the locations of knots assume their number is ...
Yan Wang,Lu Tian
Yan Wang
The performance of commonly used asymptotic inference procedures for the random effects model used in meta analysis relies on the number of studies. When the number of studies is moderate or small, the exact inference procedure is more reli...
Guangren Yang,Sumin Hou,Luheng Wang et al.
Guangren Yang et al.
The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional cova...
A density based empirical likelihood approach for testing bivariate normality [0.03%]
基于密度的双变量正态性经验似然检验方法
Gregory Gurevich,Albert Vexler
Gregory Gurevich
Sample entropy based tests, methods of sieves and Grenander estimation type procedures are known to be very efficient tools for assessing normality of underlying data distributions, in one-dimensional nonparametric settings. Recently, it ha...
Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation [0.03%]
正定矩阵集上的测地线拉格朗日蒙特卡罗方法及在贝叶斯谱密度估计中的应用
Andrew Holbrook,Shiwei Lan,Alexander Vandenberg-Rodes et al.
Andrew Holbrook et al.
We present geodesic Lagrangian Monte Carlo, an extension of Hamiltonian Monte Carlo for sampling from posterior distributions defined on general Riemannian manifolds. We apply this new algorithm to Bayesian inference on symmetric or Hermiti...
Accurate unconditional p-values for a two-arm study with binary endpoints [0.03%]
二分类终点的两臂试验的确切无条件检验量和p值计算方法研究
Guogen Shan,Le Kang,Min Xiao et al.
Guogen Shan et al.
Unconditional exact tests are increasingly used in practice for categorical data to increase the power of a study and to make the data analysis approach being consistent with the study design. In a two-arm study with a binary endpoint, p-va...
HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets [0.03%]
HDDA：DataSifter：电子健康记录及其他敏感数据集的统计屏蔽技术
Simeone Marino,Nina Zhou,Yi Zhao et al.
Simeone Marino et al.
There are no practical and effective mechanisms to share high-dimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exp...
Robust gene-environment interaction analysis using penalized trimmed regression [0.03%]
基于惩罚截断回归的稳健基因-环境交互效应分析方法研究
Yaqing Xu,Mengyun Wu,Shuangge Ma et al.
Yaqing Xu et al.
In biomedical and epidemiological studies, gene-environment (G-E) interactions have been shown to importantly contribute to the etiology and progression of many complex diseases. Most existing approaches for identifying G-E interactions are...
Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates [0.03%]
EM算法在逻辑回归模型相关自变量中贝叶斯变量选择中的应用
M D Koslovsky,M D Swartz,L Leon-Novelo et al.
M D Koslovsky et al.
We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable sel...
Statistical power to detect violation of the proportional hazards assumption when using the Cox regression model [0.03%]
利用Cox回归模型检测违反比例风险假设的统计功效
Peter C Austin
Peter C Austin
The use of the Cox proportional hazards regression model is widespread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However,...