High dimensional classification for spatially dependent data with application to neuroimaging
DOI10.1214/20-EJS1743zbMath1452.62455arXiv2005.01168MaRDI QIDQ2209817
Liangliang Zhang, Tapabrata Maiti, Ying-Jie Li
Publication date: 5 November 2020
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.01168
classificationhigh dimensionallinear discriminant analysismisclassificationneuroimagingspatially dependenttapered covariance
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Image analysis in multivariate analysis (62H35) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Statistical aspects of big data and data science (62R07)
Related Items (1)
Uses Software
Cites Work
- Covariate-Adjusted Tensor Classification in High-Dimensions
- Selection of spatial-temporal lattice models: assessing the impact of climate conditions on a mountain pine beetle outbreak
- On estimation and selection of autologistic regression models via penalized pseudolikelihood
- Sparse linear discriminant analysis by thresholding for high dimensional data
- Penalized maximum likelihood estimation and variable selection in geostatistics
- Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data
- The statistical analysis of fMRI data
- One-step sparse estimates in nonconcave penalized likelihood models
- Covariance regularization by thresholding
- High-dimensional classification using features annealed independence rules
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree
- Sparse permutation invariant covariance estimation
- Regularized estimation of large covariance matrices
- Penalized Classification using Fisher’s Linear Discriminant
- A direct approach to sparse discriminant analysis in ultra-high dimensions
- A Direct Estimation Approach to Sparse Linear Discriminant Analysis
- Maximum likelihood estimation of models for residual covariance in spatial regression
- Sparse Quadratic Discriminant Analysis For High Dimensional Data
- Optimal Geostatistical Model Selection
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- On Selection of Spatial Linear Models for Lattice Data
- Covariance-enhanced discriminant analysis
- Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging
- Variable selection for binary spatial regression: Penalized quasi‐likelihood approach
- Random-projection Ensemble Classification
- A Road to Classification in High Dimensional Space: The Regularized Optimal Affine Discriminant
- Additive Model Building for Spatial Regression
- The elements of statistical learning. Data mining, inference, and prediction
- Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation
This page was built for publication: High dimensional classification for spatially dependent data with application to neuroimaging
