Joint hierarchical Gaussian process model with application to personalized prediction in medical monitoring [0.03%]
应用于医疗监测的个性化预测联合层次高斯过程模型
Leo L Duan,Xia Wang,John P Clancy et al.
Leo L Duan et al.
A two-level Gaussian process (GP) joint model is proposed to improve personalized prediction of medical monitoring data. The proposed model is applied to jointly analyze multiple longitudinal biomedical outcomes, including continuous measur...
A procedure to detect general association based on concentration of ranks [0.03%]
一种基于等级集中的检测一般关联的程序
Pratyaydipta Rudra,Yihui Zhou,Fred A Wright
Pratyaydipta Rudra
In modern high-throughput applications, it is important to identify pairwise associations between variables, and desirable to use methods that are powerful and sensitive to a variety of association relationships. We describe RankCover, a ne...
Flexible functional regression methods for estimating individualized treatment regimes [0.03%]
灵活的功能回归方法在估计个体化治疗方案中的应用
Adam Ciarleglio,Eva Petkova,Thaddeus Tarpey et al.
Adam Ciarleglio et al.
A major focus of personalized medicine is on the development of individualized treatment rules. Good decision rules have the potential to significantly advance patient care and reduce the burden of a host of diseases. Statistical methods fo...
Semiparametric Bayes conditional graphical models for imaging genetics applications [0.03%]
用于影像遗传学应用的半参数条件图模型
Suprateek Kundu,Jian Kang
Suprateek Kundu
Motivated by the need for understanding neurological disorders, large-scale imaging genetic studies are being increasingly conducted. A salient objective in such studies is to identify important neuroimaging biomarkers such as the brain fun...
A Bayesian supervised dual-dimensionality reduction model for simultaneous decoding of LFP and spike train signals [0.03%]
一种贝叶斯监督双维性减少模型 用于LFP和脉冲序列信号的联合解码
Andrew Holbrook,Alexander Vandenberg-Rodes,Norbert Fortin et al.
Andrew Holbrook et al.
Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities-such as EEG, fMRI, LFP, and spike trains-offer different views of the complex systems contributing to neural...
Miguel Marino,Orfeu M Buxton,Yi Li
Miguel Marino
Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-sele...
Longitudinal functional additive model with continuous proportional outcomes for physical activity data [0.03%]
具有连续比例结果的纵向函数加性模型在物理活动数据中的应用
Haocheng Li,Sarah Kozey-Keadle,Victor Kipnis et al.
Haocheng Li et al.
Motivated by physical activity data obtained from the BodyMedia FIT device (www.bodymedia.com), we take a functional data approach for longitudinal studies with continuous proportional outcomes. The functional structure depends on three fac...
Yakuan Chen,Jeff Goldsmith,Todd Ogden
Yakuan Chen
For regression models with functional responses and scalar predictors, it is common for the number of predictors to be large. Despite this, few methods for variable selection exist for function-on-scalar models, and none account for the inh...
Bereket P Kindo,Hao Wang,Edsel A Peña
Bereket P Kindo
This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of BART - Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the obser...
Julia Wrobel,So Young Park,Ana Maria Staicu et al.
Julia Wrobel et al.
Although there are established graphics that accompany the most common functional data analyses, generating these graphics for each dataset and analysis can be cumbersome and time consuming. Often, the barriers to visualization inhibit usef...