Canonical correlation analysis in high dimensions with structured regularization [0.03%]
高维结构正则化的典型相关分析
Elena Tuzhilina,Leonardo Tozzi,Trevor Hastie
Elena Tuzhilina
Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ2 penalty on the CCA coefficien...
Streamlined variational inference for higher level group-specific curve models [0.03%]
用于更高层次的组特异性曲线模型的改进变分推断方法
M Menictas,T H Nolan,D G Simpson et al.
M Menictas et al.
A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another ...
Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks [0.03%]
基于半竞争风险的纵向和生存数据的贝叶斯联合模型在评估生物标志物重要性中的应用
Fan Zhang,Ming-Hui Chen,Xiuyu Julie Cong et al.
Fan Zhang et al.
Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modeling of PRO/QOL and survival data can provide a comparative assessment ...
Joint modelling of longitudinal and survival data in the presence of competing risks with applications to prostate cancer data [0.03%]
竞争风险存在下的纵向与生存数据的联合建模及其在前列腺癌数据分析中的应用
Md Tuhin Sheikh,Joseph G Ibrahim,Jonathan A Gelfond et al.
Md Tuhin Sheikh et al.
This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer o...
A Bayesian transition model for missing longitudinal binary outcomes and an application to a smoking cessation study [0.03%]
一种用于丢失的纵向二进制结果的贝叶斯转换模型及其在戒烟研究中的应用
Li Li,Ji-Hyun Lee,Steven K Sutton et al.
Li Li et al.
Smoking cessation intervention studies often produce data on smoking status at discrete follow-up assessments, often with missing data in different amounts at each assessment. Smoking status in these studies is a dynamic process with indivi...
Identifying Dynamical Time Series Model Parameters from Equilibrium Samples, with Application to Gene Regulatory Networks [0.03%]
从平衡样本识别动态时间序列模型参数及其在基因调控网络中的应用
William Chad Young,Ka Yee Yeung,Adrian E Raftery
William Chad Young
Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady state observations. T...
Tianming Gao,Jeffrey M Albert
Tianming Gao
Causal mediation analysis provides investigators insight into how a treatment or exposure can affect an outcome of interest through one or more mediators on causal pathway. When multiple mediators on the pathway are causally ordered, identi...
Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball [0.03%]
具核惩罚多元逻辑回归及其在棒球击球结果预测中的应用
Scott Powers,Trevor Hastie,Robert Tibshirani
Scott Powers
We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression. This convex relaxation of reduced-rank multinomial regression has the advantage of leveraging underlying structure among the ...
Quasi-periodic spatiotemporal models of brain activation in single-trial MEG experiments [0.03%]
单试次MEG实验中的脑激活的准周期时空模型
Massimo Ventrucci,Adrian W Bowman,Claire Miller et al.
Massimo Ventrucci et al.
Magneto-encephalography (MEG) is an imaging technique which measures neuronal activity in the brain. Even when a subject is in a resting state, MEG data show characteristic spatial and temporal patterns, resulting from electrical current at...
Rejoinder to statistical contributions to bioinformatics: Design, modelling, structure learning and Integration [0.03%]
统计学在生物信息学中的应用：设计、建模、结构学习与集成的讨论回复
Jeffrey S Morris,Veerabhadran Baladandayuthapani
Jeffrey S Morris
We thank the discussants for their kind comments and their insightful analysis and discussion that has substantially added to the contribution of this issue. Overall, it seems the discussants have affirmed many of our primary points, and ha...