Dayu Sun,Limin Peng,Zhiping Qiu et al.
Dayu Sun et al.
Tensors, characterized as multidimensional arrays, are frequently encountered in modern scientific studies. Quantile regression has the unique capacity to explore how a tensor covariate influences different segments of the response distribu...
Estimation and Inference of Quantile Spatially Varying Coefficient Models Over Complicated Domains [0.03%]
复杂区域上分位数空间变系数模型的估计和推断
Myungjin Kim,Lily Wang,Huixia Judy Wang
Myungjin Kim
This paper presents a flexible quantile spatially varying coefficient model (QSVCM) for the regression analysis of spatial data. The proposed model enables researchers to assess the dependence of conditional quantiles of the response variab...
Simultaneous inference for generalized linear models with unmeasured confounders [0.03%]
存在未测量混杂因素的广义线性模型的同步推断
Jin-Hong Du,Larry Wasserman,Kathryn Roeder
Jin-Hong Du
Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially bias...
Alexander Dombowsky,David B Dunson
Alexander Dombowsky
Bayesian clustering typically relies on mixture models, with each component interpreted as a different cluster. After defining a prior for the component parameters and weights, Markov chain Monte Carlo (MCMC) algorithms are commonly used to...
Banerjee Sudipto,Alan E Gelfand,C F Sirmans
Banerjee Sudipto
Spatial process models are now widely used for inference in many areas of application. In such contexts interest is often in the rate of change of a spatial surface at a given location in a given direction. Examples include temperature or r...
Identifying genetic variants for brain connectivity using Ball Covariance Ranking and Aggregation [0.03%]
基于球协方差排序和聚合的脑连接遗传变异识别方法
Wei Dai,Heping Zhang
Wei Dai
Understanding the genetic architecture of brain functions is essential to clarify the biological etiologies of behavioral and psychiatric disorders. Functional connectivity, representing pairwise correlations of neural activities between br...
Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART [0.03%]
基于BART的条件交叉-over设计中估计异质性效应
Jacob R Englert,Stefanie T Ebelt,Howard H Chang
Jacob R Englert
Epidemiological approaches for examining human health responses to environmental exposures in observational studies often control for confounding by implementing clever matching schemes and using statistical methods based on conditional lik...
Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models [0.03%]
缺失非随机条件下的矩阵补全及其在因果面板数据模型中的应用
Jungjun Choi,Ming Yuan
Jungjun Choi
This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enoug...
Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis [0.03%]
基于子空间因子分析的多源数据联合协方差矩阵估计方法研究
Noirrit Kiran Chandra,David B Dunson,Jason Xu
Noirrit Kiran Chandra
Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are often collected under different conditions, f...
Who Are We Missing?: A Principled Approach to Characterizing the Underrepresented Population [0.03%]
谁被忽略了？一种表征不足人群的系统方法
Harsh Parikh,Rachael K Ross,Elizabeth Stuart et al.
Harsh Parikh et al.
Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the c...