A simultaneous confidence-bounded true discovery proportion perspective on localizing differences in smooth terms in regression models [0.03%]
从回归模型中平滑项差异定位的角度看置信度限制的真正发现比例问题
David Swanson
David Swanson
A method is demonstrated for localizing where two spline terms, or smooths, differ using a true discovery proportion (TDP)-based interpretation. The procedure yields a statement on the proportion of some region where true differences exist ...
Sparse vertex discriminant analysis: Variable selection for biomedical classification applications [0.03%]
稀疏顶点判别分析：生物医学分类应用中的变量选择
Alfonso Landeros,Seyoon Ko,Jack Z Chang et al.
Alfonso Landeros et al.
Modern biomedical datasets are often high-dimensional at multiple levels of biological organization. Practitioners must therefore grapple with data to estimate sparse or low-rank structures so as to adhere to the principle of parsimony. Fur...
MIXANDMIX: numerical techniques for the computation of empirical spectral distributions of population mixtures [0.03%]
人口混合群体的经验光谱分布的计算数值技术
Lucilio Cordero-Grande
Lucilio Cordero-Grande
The MIXANDMIX (mixtures by Anderson mixing) tool for the computation of the empirical spectral distribution of random matrices generated by mixtures of populations is described. Within the population mixture model the mapping between the po...
Locally sparse quantile estimation for a partially functional interaction model [0.03%]
部分函数交互模型下的分位数估计方法
Weijuan Liang,Qingzhao Zhang,Shuangge Ma
Weijuan Liang
Functional data analysis has been extensively conducted. In this study, we consider a partially functional model, under which some covariates are scalars and have linear effects, while some other variables are functional and have unspecifie...
Flexible Regularized Estimation in High-Dimensional Mixed Membership Models [0.03%]
高维混合体模型中的灵活正则化估计方法
Nicholas Marco,Damla Şentürk,Shafali Jeste et al.
Nicholas Marco et al.
Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mixed membership models of high-dimensional continuous data is ...
GPU Accelerated Estimation of a Shared Random Effect Joint Model for Dynamic Prediction [0.03%]
基于共享随机效应的联合模型的动态预测的GPU加速估计方法
Shikun Wang,Zhao Li,Lan Lan et al.
Shikun Wang et al.
In longitudinal cohort studies, it is often of interest to predict the risk of a terminal clinical event using longitudinal predictor data among subjects at risk by the time of the prediction. The at-risk population changes over time; so do...
Semiparametric function-on-function quantile regression model with dynamic single-index interactions [0.03%]
具有动态单指数交互的半参数函数-函数分位数回归模型
Hanbing Zhu,Yuanyuan Zhang,Yehua Li et al.
Hanbing Zhu et al.
In this paper we propose a new semiparametric function-on-function quantile regression model with time-dynamic single-index interactions. Our model is very flexible in taking into account of the nonlinear time-dynamic interaction effects of...
Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data [0.03%]
基于多组学数据的贝叶斯联合因子分解和预测方法
Sarah Samorodnitsky,Chris H Wendt,Eric F Lock
Sarah Samorodnitsky
Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no avail...
Spectral Clustering via sparse graph structure learning with application to Proteomic Signaling Networks in Cancer [0.03%]
基于稀疏图学习的谱聚类及其在癌症蛋白质信号网络中的应用
Sayantan Banerjee,Rehan Akbani,Veerabhadran Baladandayuthapani
Sayantan Banerjee
Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge stru...
Fei Zhou,Jie Ren,Shuangge Ma et al.
Fei Zhou et al.
The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the respon...