Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces [0.03%]
度量空间中通过度量分布函数进行非参数统计推断
Xueqin Wang,Jin Zhu,Wenliang Pan et al.
Xueqin Wang et al.
The distribution function is essential in statistical inference and connected with samples to form a directed closed loop by the correspondence theorem in measure theory and the Glivenko-Cantelli and Donsker properties. This connection crea...
A Minimax Optimal Ridge-Type Set Test for Global Hypothesis with Applications in Whole Genome Sequencing Association Studies [0.03%]
一种最小最大最优的岭型集合检验法在全基因组关联研究中全局假设的应用
Yaowu Liu,Zilin Li,Xihong Lin
Yaowu Liu
Testing a global hypothesis for a set of variables is a fundamental problem in statistics with a wide range of applications. A few well-known classical tests include the Hotelling's T 2 test, the F test, and the empirical Bayes based sco...
Estimation of the number of spiked eigenvalues in a covariance matrix by bulk eigenvalue matching analysis [0.03%]
通过bulk特征值匹配分析估计协方差矩阵中尖峰特征值的数量
Zheng Tracy Ke,Yucong Ma,Xihong Lin
Zheng Tracy Ke
The spiked covariance model has gained increasing popularity in high-dimensional data analysis. A fundamental problem is determination of the number of spiked eigenvalues, K . For estimation of K , most attention has focused on the use of t...
Ming-Hui Chen,Joseph G Ibrahim,Debajyoti Sinha
Ming-Hui Chen
We consider Bayesian methods for right-censored survival data for populations with a surviving (cure) fraction. We propose a model that is quite different from the standard mixture model for cure rates. We provide a natural motivation and i...
Li Chen,Chunlin Li,Xiaotong Shen et al.
Li Chen et al.
This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferentia...
Qi Zhang,Lingzhou Xue,Bing Li
Qi Zhang
With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fréchet regression model (Peterson & Müller 2019) provides a pr...
A Regression-based Approach to Robust Estimation and Inference for Genetic Covariance [0.03%]
基于回归的遗传协方差 robust 估计和推断方法
Jianqiao Wang,Sai Li,Hongzhe Li
Jianqiao Wang
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined...
Model-robust and efficient covariate adjustment for cluster-randomized experiments [0.03%]
具有效性和稳健性的分层随机实验协变量调整方法
Bingkai Wang,Chan Park,Dylan S Small et al.
Bingkai Wang et al.
Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of m...
Antik Chakraborty,Rihui Ou,David B Dunson
Antik Chakraborty
It has become increasingly common to collect high-dimensional binary response data; for example, with the emergence of new sampling techniques in ecology. In smaller dimensions, multivariate probit (MVP) models are routinely used for infere...
Crime in Philadelphia: Bayesian Clustering with Particle Optimization [0.03%]
费城犯罪问题的贝叶斯聚类及其粒子优化方法研究
Cecilia Balocchi,Sameer K Deshpande,Edward I George et al.
Cecilia Balocchi et al.
Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crim...