An overview of the estimation of large covariance and precision matrices
The estimation of large covariance and precision matrices is fundamental in modern
multivariate analysis. However, problems arise from the statistical analysis of large panel …
multivariate analysis. However, problems arise from the statistical analysis of large panel …
Functional regression
JS Morris - Annual Review of Statistics and Its Application, 2015 - annualreviews.org
Functional data analysis (FDA) involves the analysis of data whose ideal units of
observation are functions defined on some continuous domain, and the observed data …
observation are functions defined on some continuous domain, and the observed data …
Large covariance estimation by thresholding principal orthogonal complements
J Fan, Y Liao, M Mincheva - Journal of the Royal Statistical …, 2013 - academic.oup.com
The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …
[图书][B] Mathematical statistics: basic ideas and selected topics, volumes I-II package
This package includes both Mathematical Statistics: Basic Ideas and Selected Topics,
Volume I, Second Edition, as well as Mathematical Statistics: Basic Ideas and Selected …
Volume I, Second Edition, as well as Mathematical Statistics: Basic Ideas and Selected …
A selective overview of sparse principal component analysis
Principal component analysis (PCA) is a widely used technique for dimension reduction,
data processing, and feature extraction. The three tasks are particularly useful and important …
data processing, and feature extraction. The three tasks are particularly useful and important …
Sparse PCA: Optimal rates and adaptive estimation
Sparse PCA: Optimal rates and adaptive estimation Page 1 The Annals of Statistics 2013, Vol.
41, No. 6, 3074–3110 DOI: 10.1214/13-AOS1178 © Institute of Mathematical Statistics, 2013 …
41, No. 6, 3074–3110 DOI: 10.1214/13-AOS1178 © Institute of Mathematical Statistics, 2013 …
[HTML][HTML] Distributed estimation of principal eigenspaces
Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts
latent principal factors that contribute to the most variation of the data. When data are stored …
latent principal factors that contribute to the most variation of the data. When data are stored …
[PDF][PDF] Truncated Power Method for Sparse Eigenvalue Problems.
This paper considers the sparse eigenvalue problem, which is to extract dominant (largest)
sparse eigenvectors with at most k non-zero components. We propose a simple yet effective …
sparse eigenvectors with at most k non-zero components. We propose a simple yet effective …
Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation
This is an expository paper that reviews recent developments on optimal estimation of
structured high-dimensional covariance and precision matrices. Minimax rates of …
structured high-dimensional covariance and precision matrices. Minimax rates of …
Optimal detection of sparse principal components in high dimension
Q Berthet, P Rigollet - 2013 - projecteuclid.org
We perform a finite sample analysis of the detection levels for sparse principal components
of a high-dimensional covariance matrix. Our minimax optimal test is based on a sparse …
of a high-dimensional covariance matrix. Our minimax optimal test is based on a sparse …