Estimation of autocovariance matrices for high dimensional linear processes
From MaRDI portal
Publication:2036316
DOI10.1007/s00184-020-00790-2zbMath1471.62373OpenAlexW3080328277MaRDI QIDQ2036316
Publication date: 28 June 2021
Published in: Metrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00184-020-00790-2
Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- High dimensional covariance matrix estimation using a factor model
- Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
- Optimal rates of convergence for covariance matrix estimation
- Covariance regularization by thresholding
- Consistency of large dimensional sample covariance matrix under weak dependence
- Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension
- Covariance and precision matrix estimation for high-dimensional time series
- Regularized estimation of large covariance matrices
- ESTIMATION OF AUTOCOVARIANCE MATRICES FOR INFINITE DIMENSIONAL VECTOR LINEAR PROCESS
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Banded and tapered estimates for autocovariance matrices and the linear process bootstrap
- Introduction to Time Series and Forecasting
- On Consistency and Sparsity for Principal Components Analysis in High Dimensions
- Generalized Thresholding of Large Covariance Matrices
- Covariance matrix selection and estimation via penalised normal likelihood
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
This page was built for publication: Estimation of autocovariance matrices for high dimensional linear processes