The Annals of Statistics
- Ann. Statist.
- Volume 41, Number 6 (2013), 2994-3021.
Covariance and precision matrix estimation for high-dimensional time series
We consider estimation of covariance matrices and their inverses (a.k.a. precision matrices) for high-dimensional stationary and locally stationary time series. In the latter case the covariance matrices evolve smoothly in time, thus forming a covariance matrix function. Using the functional dependence measure of Wu [Proc. Natl. Acad. Sci. USA 102 (2005) 14150–14154 (electronic)], we obtain the rate of convergence for the thresholded estimate and illustrate how the dependence affects the rate of convergence. Asymptotic properties are also obtained for the precision matrix estimate which is based on the graphical Lasso principle. Our theory substantially generalizes earlier ones by allowing dependence, by allowing nonstationarity and by relaxing the associated moment conditions.
Ann. Statist., Volume 41, Number 6 (2013), 2994-3021.
First available in Project Euclid: 1 January 2014
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62H12: Estimation
Secondary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
High-dimensional inference sparsity covariance matrix precision matrix thresholding Lasso dependence functional dependence measure consistency Nagaev inequality nonstationary time series spatial–temporal processes
Chen, Xiaohui; Xu, Mengyu; Wu, Wei Biao. Covariance and precision matrix estimation for high-dimensional time series. Ann. Statist. 41 (2013), no. 6, 2994--3021. doi:10.1214/13-AOS1182. https://projecteuclid.org/euclid.aos/1388545676
- Supplementary material: Additional proofs. The supplementary file contains the proof of relation (64): spectral norm convergence rate for precision matrix.