A0961
Title: Estimation of spectra of high-dimensional separable covariance matrices
Authors: Lili Wang - Zhejiang Gongshang University (China) [presenting]
Debashis Paul - University of California, Davis (United States)
Abstract: The aim is to estimate the joint spectra of high-dimensional time series for which the observed data matrix is assumed to have a separable covariance structure. The primary interest is in estimating the distribution of the eigenvalues of the marginal covariance of the observation vectors under partial information, such as stationarity or sparsity, on the temporal covariance structure. A method that utilizes random matrix theory is developed to estimate the unknown population spectra by repressing the spectrum of the dimensional covariance matrix on a simplex. The consistency of the proposed estimator is proven under the dimension proportional to the sample size setting. Furthermore, a resampling-based method is developed for statistical inference on low-dimensional functionals of the joint spectrum of the population covariance matrix.
