On singular values of large dimensional lag- [Formula: see text] sample auto-correlation matrices [0.03%]
大维Lag-k自相关矩阵的奇异值分布
Zhanting Long,Zeng Li,Ruitao Lin et al.
Zhanting Long et al.
We study the limiting behavior of singular values of a lag-τ sample auto-correlation matrix Rτϵ of large dimensional vector white noise process, the error term ϵ in the high-dimensional factor model. We establish the l...
Surface Functional Models [0.03%]
表面功能模型
Ziqi Chen,Jianhua Hu,Hongtu Zhu
Ziqi Chen
The aim of this paper is to develop a new framework of surface functional models (SFM) for surface functional data which contains repeated observations in two domains (typically, time-location). The primary problem of interest is to investi...
Kuangnan Fang,Yuanxing Chen,Shuangge Ma et al.
Kuangnan Fang et al.
In biomedical data analysis, clustering is commonly conducted. Biclustering analysis conducts clustering in both the sample and covariate dimensions and can more comprehensively describe data heterogeneity. In most of the existing bicluster...
Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data [0.03%]
用于密度和形状的切线功能典型相关分析及其在多模态影像数据分析中的应用
Min Ho Cho,Sebastian Kurtek,Karthik Bharath
Min Ho Cho
It is quite common for functional data arising from imaging data to assume values in infinite-dimensional manifolds. Uncovering associations between two or more such nonlinear functional data extracted from the same object across medical im...
Meng Li,Kehui Wang,Arnab Maity et al.
Meng Li et al.
In this paper, we study statistical inference in functional quantile regression for scalar response and a functional covariate. Specifically, we consider a functional linear quantile regression model where the effect of the covariate on the...
High Dimensional Change Point Inference: Recent Developments and Extensions [0.03%]
高维变化点推断： recent developments和扩展
Bin Liu,Xinsheng Zhang,Yufeng Liu
Bin Liu
Change point analysis aims to detect structural changes in a data sequence. It has always been an active research area since it was introduced in the 1950s. In modern statistical applications, however, high-throughput data with increasing d...
Nonparametric spectral methdods for multivariate spatial and spatial-temporal data [0.03%]
多元空间和时空数据的非参数谱方法
Joseph Guinness
Joseph Guinness
We propose computationally efficient methods for estimating stationary multivariate spatial and spatial-temporal spectra from incomplete gridded data. The methods are iterative and rely on successive imputation of data and updating of model...
A note on bias due to fitting prospective multivariate generalized linear models to categorical outcomes ignoring retrospective sampling schemes [0.03%]
关于对分类结果忽略回顾式抽样方案拟合前瞻式多变量广义线性模型造成偏倚的注记
Bhramar Mukherjee,Ivy Liu
Bhramar Mukherjee
Outcome dependent sampling designs are commonly used in economics, market research and epidemiological studies. Case-control sampling design is a classic example of outcome dependent sampling, where exposure information is collected on subj...
Variable selection for partially linear models via Bayesian subset modeling with diffusing prior [0.03%]
基于扩散先验的贝叶斯子集模型在部分线性模型变量选择中的应用
Jia Wang,Xizhen Cai,Runze Li
Jia Wang
Most existing methods of variable selection in partially linear models (PLM) with ultrahigh dimensional covariates are based on partial residuals, which involve a two-step estimation procedure. While the estimation error produced in the fir...
Benjamin W Langworthy,Rebecca L Stephens,John H Gilmore et al.
Benjamin W Langworthy et al.
Canonical correlation analysis (CCA) is a common method used to estimate the associations between two different sets of variables by maximizing the Pearson correlation between linear combinations of the two sets of variables. We propose a v...