Smoothing and Mean-Covariance Estimation of Functional Data with a Bayesian Hierarchical Model [0.03%]
基于贝叶斯分层模型的功能数据平滑及均值和协方差估计
Jingjing Yang,Hongxiao Zhu,Taeryon Choi et al.
Jingjing Yang et al.
Functional data, with basic observational units being functions (e.g., curves, surfaces) varying over a continuum, are frequently encountered in various applications. While many statistical tools have been developed for functional data anal...
A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes [0.03%]
基于延迟结果的I-II期篮式试验设计以优化剂量-时间模式
Ruitao Lin,Peter F Thall,Ying Yuan
Ruitao Lin
This paper proposes a Bayesian adaptive basket trial design to optimize the dose-schedule regimes of an experimental agent within disease subtypes, called "baskets", for phase I-II clinical trials based on late-onset efficacy and toxicity. ...
On the Existence of Uniformly Most Powerful Bayesian Tests With Application to Non-Central Chi-Squared Tests [0.03%]
关于均匀最优贝叶斯检验的存在性及其在非中心卡方检验中的应用
Amir Nikooienejad,Valen E Johnson
Amir Nikooienejad
Uniformly most powerful Bayesian tests (UMPBT's) are an objective class of Bayesian hypothesis tests that can be considered the Bayesian counterpart of classical uniformly most powerful tests. Because the rejection regions of UMPBT's can be...
Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices [0.03%]
灵活的贝叶斯动态相关和协方差矩阵建模
Shiwei Lan,Andrew Holbrook,Gabriel A Elias et al.
Shiwei Lan et al.
Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality. Our approach is to decompose the covariance matrix into the correlation and variance matrices a...
Bayesian Analysis of RNA-Seq Data Using a Family of Negative Binomial Models [0.03%]
基于负二项分布族模型的RNA测序数据的贝叶斯分析方法研究
Lili Zhao,Weisheng Wu,Dai Feng et al.
Lili Zhao et al.
The analysis of RNA-Seq data has been focused on three main categories, including gene expression, relative exon usage and transcript expression. Methods have been proposed independently for each category using a negative binomial (NB) mode...
Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models [0.03%]
基于有向无环图自回归(DAGAR)模型的空间疾病制图
Abhirup Datta,Sudipto Banerjee,James S Hodges et al.
Abhirup Datta et al.
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorpor...
A New Bayesian Single Index Model with or without Covariates Missing at Random [0.03%]
一种新的贝叶斯单指标模型及其随机缺失性covariates处理方法
Kumaresh Dhara,Stuart Lipsitz,Debdeep Pati et al.
Kumaresh Dhara et al.
For many biomedical, environmental, and economic studies, the single index model provides a practical dimension reaction as well as a good physical interpretation of the unknown nonlinear relationship between the response and its multiple p...
Bayesian Zero-Inflated Negative Binomial Regression Based on Pólya-Gamma Mixtures [0.03%]
基于Polya-Gamma混合模型的贝叶斯零膨胀负二项回归
Brian Neelon
Brian Neelon
Motivated by a study examining spatiotemporal patterns in inpatient hospitalizations, we propose an efficient Bayesian approach for fitting zero-inflated negative binomial models. To facilitate posterior sampling, we introduce a set of late...
Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies [0.03%]
利用贝叶斯潜在高斯图形模型推理语言死因调查中的症状关联
Zehang Richard Li,Tyler H McComick,Samuel J Clark
Zehang Richard Li
Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in statistics. Existing methods, however, typically rely on copious, high quality data to a...
Suprateek Kundu,Bani K Mallick,Veera Baladandayuthapan
Suprateek Kundu
There has been an intense development in the Bayesian graphical model literature over the past decade; however, most of the existing methods are restricted to moderate dimensions. We propose a novel graphical model selection approach for la...