Seonjoo Lee,Haipeng Shen,Young Truong
Seonjoo Lee
Independent Component Analysis (ICA) offers an effective data-driven approach for blind source extraction encountered in many signal and image processing problems. Although many ICA methods have been developed, they have received relatively...
Distributed Simultaneous Inference in Generalized Linear Models via Confidence Distribution [0.03%]
广义线性模型中基于置信分布的分布式同时推断
Lu Tang,Ling Zhou,Peter X-K Song
Lu Tang
We propose a distributed method for simultaneous inference for datasets with sample size much larger than the number of covariates, i.e., N ≫ p, in the generalized linear models framework. When such datasets are too big to be analyzed enti...
Model-based clustering of time-evolving networks through temporal exponential-family random graph models [0.03%]
基于时间指数族随机图模型的时变网络的模型聚类分析
Kevin H Lee,Lingzhou Xue,David R Hunter
Kevin H Lee
Dynamic networks are a general language for describing time-evolving complex systems, and discrete time network models provide an emerging statistical technique for various applications. It is a fundamental research question to detect a set...
Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model [0.03%]
广义尖峰总体模型下主成分分析的渐近性质及收缩偏差校正
Rounak Dey,Seunggeun Lee
Rounak Dey
With the development of high-throughput technologies, principal component analysis (PCA) in the high-dimensional regime is of great interest. Most of the existing theoretical and methodological results for high-dimensional PCA are based on ...
Generalized Linear Mixed Models with Gaussian Mixture Random Effects: Inference and Application [0.03%]
具有高斯混合随机器的广义线性混合模型的推理和应用
Lanfeng Pan,Yehua Li,Kevin He et al.
Lanfeng Pan et al.
We propose a new class of generalized linear mixed models with Gaussian mixture random effects for clustered data. To overcome the weak identifiability issues, we fit the model using a penalized Expectation Maximization (EM) algorithm, and ...
Graph-based sparse linear discriminant analysis for high-dimensional classification [0.03%]
基于图的稀疏线性判别分析在高维分类中的应用
Jianyu Liu,Guan Yu,Yufeng Liu
Jianyu Liu
Linear discriminant analysis (LDA) is a well-known classification technique that enjoyed great success in practical applications. Despite its effectiveness for traditional low-dimensional problems, extensions of LDA are necessary in order t...
Feature screening in ultrahigh-dimensional varying-coefficient Cox model [0.03%]
超高维变系数Cox模型的特征筛选方法研究
Guangren Yang,Ling Zhang,Runze Li et al.
Guangren Yang et al.
The varying-coefficient Cox model is flexible and useful for modeling the dynamic changes of regression coefficients in survival analysis. In this paper, we study feature screening for varying-coefficient Cox models in ultrahigh-dimensional...
A semiparametric efficient estimator in case-control studies for gene-environment independent models [0.03%]
病例对照研究中基因环境独立模型的半参数高效估计方法
Liang Liang,Yanyuan Ma,Raymond J Carroll
Liang Liang
Case-controls studies are popular epidemiological designs for detecting gene-environment interactions in the etiology of complex diseases, where the genetic susceptibility and environmental exposures may often be reasonably assumed independ...
Jingwei Wu,Hanxiang Peng,Wanzhu Tu
Jingwei Wu
By optimizing index functions against different outcomes, we propose a multivariate single-index model (SIM) for development of medical indices that simultaneously work with multiple outcomes. Fitting of a multivariate SIM is not fundamenta...
Roy's largest root under rank-one perturbations: the complex valued case and applications [0.03%]
Rank-one扰动下的Roy最大根：复值情况及其应用
Prathapasinghe Dharmawansa,Boaz Nadler,Ofer Shwartz
Prathapasinghe Dharmawansa
The largest eigenvalue of a single or a double Wishart matrix, both known as Roy's largest root, plays an important role in a variety of applications. Recently, via a small noise perturbation approach with fixed dimension and degrees of fre...