Parker Knight,Ndey Isatou Jobe,Rui Duan
Parker Knight
Statistical integration of diverse data sources is an essential step in the building of generalizable prediction tools, especially in precision health. The invariant features model is a new paradigm for multi-source data integration which p...
Wenbo Ouyang,Ruiyang Wu,Ning Hao et al.
Wenbo Ouyang et al.
This paper introduces a novel framework for dynamic classification in high dimensional spaces, addressing the evolving nature of class distributions over time or other index variables. Traditional discriminant analysis techniques are adapte...
Subharup Guha,Peihua Qiu
Subharup Guha
A fundamental task in the automated analysis of images is the development of effective image pair comparison techniques. For two high-dimensional images, a statistical method must automatically label them as "similar" or "different" dependi...
Yuanxing Chen,Qingzhao Zhang,Shuangge Ma
Yuanxing Chen
In functional data analysis, unsupervised clustering has been extensively conducted and has important implications. In most of the existing functional clustering analyses, it is assumed that there is a single clustering structure across the...
Jan Graffelman
Jan Graffelman
The visualization of the correlation matrix by means of biplots is considered. The classical centering operations, either by the overall mean, the column means, or row and column means are shown to be problematic for the visualisation of th...
Extrapolation before imputation reduces bias when imputing censored covariates [0.03%]
插补前的外推可降低截断协变量插补时产生的偏差
Sarah C Lotspeich,Tanya P Garcia
Sarah C Lotspeich
Modeling symptom progression to identify ideal subjects for a Huntington's disease clinical trial is problematic since time to diagnosis, a key covariate, can be heavily censored. Imputation is an appealing strategy that replaces the censor...
Fan Bi,Jianan Zhu,Yang Feng
Fan Bi
In this work, we develop a new ensemble learning framework, multi-label Random Subspace Ensemble (mRaSE), for multi-label classification. Given a base classifier (e.g., multinomial logistic regression, classification tree, K-nearest neighbo...
Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees [0.03%]
多元概率公理bayesian additive回归树的增广抽样器
Yizhen Xu,Joseph Hogan,Michael Daniels et al.
Yizhen Xu et al.
The multinomial probit (MNP) (Imai and van Dyk, 2005) framework is based on a multivariate Gaussian latent structure, allowing for natural extensions to multilevel modeling. Unlike multinomial logistic models, MNP does not assume independen...
Missing Value Imputation in Relational Data using Variational Inference [0.03%]
基于变分推理的关联数据缺失值填充方法研究
Simon Fontaine,Jian Kang,Ji Zhu
Simon Fontaine
In real-world networks, node attributes are often only partially observed, necessitating imputation to support analysis or enable downstream tasks. However, most existing imputation methods overlook the rich information contained within the...
Sensitivity Analysis for Binary Outcome Misclassification in Randomization Tests via Integer Programming [0.03%]
基于整数规划的二元结果误分类的随机化检验敏感性分析
Siyu Heng,Pamela A Shaw
Siyu Heng
Conducting a randomization test is a common method for testing causal null hypotheses in randomized experiments. The popularity of randomization tests is largely because their statistical validity only depends on the randomization design, a...