Lan Wang,Bo Peng,Runze Li
Lan Wang
This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T2 test, have been proposed in the li...
Chao Huang,Martin Styner,Hongtu Zhu
Chao Huang
An important goal in image analysis is to cluster and recognize objects of interest according to the shapes of their boundaries. Clustering such objects faces at least four major challenges including a curved shape space, a high-dimensional...
Karl Bruce Gregory,Raymond J Carroll,Veerabhadran Baladandayuthapani et al.
Karl Bruce Gregory et al.
We develop a test statistic for testing the equality of two population mean vectors in the "large-p-small-n" setting. Such a test must surmount the rank-deficiency of the sample covariance matrix, which breaks down the classic Hotelling T2 ...
Ning Hao,Hao Helen Zhang
Ning Hao
In ultra-high dimensional data analysis, it is extremely challenging to identify important interaction effects, and a top concern in practice is computational feasibility. For a data set with n observations and p predictors, the augmented d...
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models [0.03%]
sparse 超高维变系数模型中的非参独立筛查方法
Jianqing Fan,Yunbei Ma,Wei Dai
Jianqing Fan
The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selec...
Fang Han,Han Liu
Fang Han
We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high dimensional non-Gaussian data. Compared with sparse PCA, our method has weaker modeling assumption and is more robust to pos...
Liping Zhu,Lexin Li,Runze Li et al.
Liping Zhu et al.
With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening pr...
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models [0.03%]
sparse 超高维加性模型的非参独立筛法
Jianqing Fan,Yang Feng,Rui Song
Jianqing Fan
A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear....
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics [0.03%]
高维稀疏因子建模在基因表达遗传学中的应用
Carlos M Carvalho,Jeffrey Chang,Joseph E Lucas et al.
Carlos M Carvalho et al.
We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and co...