A LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING [0.03%]
一种用于化学回收的成分与成分回归的潜在变量混合模型
Nicholas Rios,Lingzhou Xue,Xiang Zhan
Nicholas Rios
It is quite common to encounter compositional data in a regression framework in data analysis. When both responses and predictors are compositional, most existing models rely on a family of log-ratio based transformations to move the analys...
Bayesian multivariate sparse functional principal components analysis with application to longitudinal microbiome multiomics data [0.03%]
具有应用价值的纵向微生物组多组学数据分析的贝叶斯多元稀疏函数主成分分析
Lingjing Jiang,Chris Elrod,Jane J Kim et al.
Lingjing Jiang et al.
Microbiome researchers often need to model the temporal dynamics of multiple complex, nonlinear outcome trajectories simultaneously. This motivates our development of multivariate Sparse Functional Principal Components Analysis (mSFPCA), ex...
BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA [0.03%]
坦桑尼亚北部具有临床意义的脓毒症表型的贝叶斯学习方法
Alexander Dombowsky,David B Dunson,Deng B Madut et al.
Alexander Dombowsky et al.
Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum of distinct subtypes, motivating several studies to identif...
INFERRING SYNERGISTIC AND ANTAGONISTIC INTERACTIONS IN MIXTURES OF EXPOSURES [0.03%]
推测暴露混合物的协同和拮抗效应
Shounak Chattopadhyay,Stephanie M Engel,David Dunson
Shounak Chattopadhyay
There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse ...
BAYESIAN DATA AUGMENTATION FOR RECURRENT EVENTS UNDER INTERMITTENT ASSESSMENT IN OVERLAPPING INTERVALS WITH APPLICATIONS TO EMR DATA [0.03%]
基于间歇性评估的时间段内复发事件的贝叶斯数据增广及其在电子病历数据中的应用研究
Xin Liu,Patrick M Schnell
Xin Liu
Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but is collected primarily for purposes other than research. For recurrent events, EMR data often do not record event times or counts ...
SPATIAL PREDICTIONS ON PHYSICALLY CONSTRAINED DOMAINS: APPLICATIONS TO ARCTIC SEA SALINITY DATA [0.03%]
受物理约束的域上的空间预测及其在北极海盐度数据中的应用
Bora Jin,Amy H Herring,David Dunson
Bora Jin
In this paper we predict sea surface salinity (SSS) in the Arctic Ocean based on satellite measurements. SSS is a crucial indicator for ongoing changes in the Arctic Ocean and can offer important insights about climate change. We particular...
BAYESIAN DIFFERENTIAL CAUSAL DIRECTED ACYCLIC GRAPHS FOR OBSERVATIONAL ZERO-INFLATED COUNTS WITH AN APPLICATION TO TWO-SAMPLE SINGLE-CELL DATA [0.03%]
应用于两样本单细胞数据的贝叶斯差异因果有向无环图处理观测零膨胀计数的方法
Junsouk Choi,Robert S Chapkin,Yang Ni
Junsouk Choi
Observational zero-inflated count data arise in a wide range of areas such as genomics. One of the common research questions is to identify causal relationships by learning the structure of a sparse directed acyclic graph (DAG). While struc...
MIXED MODELING APPROACH FOR CHARACTERIZING THE GENETIC EFFECTS IN A LONGITUDINAL PHENOTYPE [0.03%]
纵向表型遗传效应特征的混合模型方法研究
Pei Zhang,Paul S Albert,Hyokyoung G Hong
Pei Zhang
Approaches for estimating genetic effects at the individual level often focus on analyzing phenotypes at a single time point, with less attention given to longitudinal phenotypes. This paper introduces a mixed modeling approach that include...
DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS [0.03%]
基于纵向连续型和离散型生物标志物的宫颈癌前病变动态风险预测模型研究
Siddharth Roy,Anindya Roy,Megan A Clarke et al.
Siddharth Roy et al.
Dynamic risk prediction that incorporates longitudinal measurements of biomarkers is useful in identifying high-risk patients for better clinical management. Our work is motivated by the prediction of cervical precancers. Currently, Pap cyt...
A QUANTITATIVE LINGUISTIC ANALYSIS OF A CANCER ONLINE HEALTH COMMUNITY WITH A SMOOTH LATENT SPACE MODEL [0.03%]
基于光滑潜在空间模型的癌症在线健康社区的语言量化分析
Mengque Liu,Xinyan Fan,Shuangge Ma
Mengque Liu
Online health communities (OHCs) provide free, open, and well-resourced platforms for patients, family members, and others to discuss illnesses, express feelings, and connect with others. Linguistic analysis of OHC posts can assist in bette...