Auteurs : Mikosch, Thomas (Auteur de la Conférence)
CIRM (Editeur )
Résumé :
We give an asymptotic theory for the eigenvalues of the sample covariance matrix of a multivariate time series. The time series constitutes a linear process across time and between components. The input noise of the linear process has regularly varying tails with index $\alpha \in \left ( 0,4 \right )$; in particular, the time series has infinite fourth moment. We derive the limiting behavior for the largest eigenvalues of the sample covariance matrix and show point process convergence of the normalized eigenvalues. The limiting process has an explicit form involving points of a Poisson process and eigenvalues of a non-negative denite matrix. Based on this convergence we derive limit theory for a host of other continuous functionals of the eigenvalues, including the joint convergence of the largest eigenvalues, the joint convergence of the largest eigenvalue and the trace of the sample covariance matrix, and the ratio of the largest eigenvalue to their sum. This is joint work with Richard A. Davis (Columbia NY) and Oliver Pfaffel (Munich).
Codes MSC :
60G55
- Point processes
62G32
- Statistics of extreme values; tail inference
|
Informations sur la Rencontre
Nom de la rencontre : Extreme value theory and laws of rare events / Théorie des valeurs extrêmes et lois des évènements rares Organisateurs de la rencontre : Freitas, Ana Cristina ; Freitas, Jorge ; Todd, Michael J. ; Vaienti, Sandro Dates : 14/07/14 - 18/07/14
Année de la rencontre : 2014
URL Congrès : http://www.mcs.st-and.ac.uk/~miket/CIRM_...
DOI : 10.24350/CIRM.V.18538803
Citer cette vidéo:
Mikosch, Thomas (2014). Asymptotic theory for the sample covariance matrix of a heavy-tailed multivariate time series. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18538803
URI : http://dx.doi.org/10.24350/CIRM.V.18538803
|
Bibliographie
- Anderson, T. W. Asymptotic Theory for Principal Component Analysis. The Annals of Mathematical Statistics 34 (1963), no. 1, 122-148 - http://dx.doi.org/10.1214/aoms/1177704248
- Auffinger, A., Ben Arous, G., Péché, S. Poisson convergence for the largest eigenvalues of heavy tailed random matrices. Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 45 (2009), no. 3, 589-610 - https://www.zbmath.org/?q=an:1177.15037
- Davis, R. A., Pfaffel, O., Stelzer, R. Limit theory for the largest eigenvalues of sample covariance matrices with heavy-tails. Stochastic Processes and their Applications, Volume 124, (2014), no. 1, 18-50 - http://dx.doi.org/10.1016/j.spa.2013.07.005
- Johnstone, I. M. On the distribution of the largest eigenvalue in principal components analysis. The Annals of Statistics 29 (2001), no. 2, 295-327 - http://dx.doi.org/10.1214/aos/1009210544
- Nagaev, S. V. Large Deviations of Sums of Independent Random Variables. The Annals of Probability 7 (1979), no. 5, 745-789 - http://dx.doi.org/10.1214/aop/1176994938
- Resnick, Sidney I. Heavy-Tail Phenomena: Probabilistic and Statistical Modeling. Springer Series in Operations Research and Financial Engineering (2007) - https://www.zbmath.org/?q=an:1152.62029
- Soshnikov, A. Poisson Statistics for the Largest Eigenvalues in Random Matrix Ensembles. Mathematical Physics of Quantum Mechanics, 2006, p. 351-364. - https://www.zbmath.org/?q=an:1169.15302
- Tao, T. and Vu, V. Random covariance matrices; universality of local statistics of eigenvalues.
Ann. Probab. 40 (2012), 1285-1315 - https://www.zbmath.org/?q=an:1247.15036