A0808
Title: CPSCA: Conditional principal spectral component analysis for covariate-dependent multivariate time series
Authors: Zeda Li - City University of New York (United States) [presenting]
Abstract: A novel frequency-domain dimension reduction method is introduced for covariate-dependent high-dimensional time series, named conditional principal spectral component analysis (CPSCA). This method decomposes the covariate-dependent multivariate time series into two parts: one with spectral densities dependent on the covariates and the other with spectral densities independent of the covariates. To uncover the latent frequency-domain dependent structures, a new metric called the spectral martingale difference divergence matrix (specMDDM) is proposed. The proposed method can serve as an initial step in the analysis of covariate-dependent high-dimensional time series, transforming a potentially high-dimensional problem into a lower-dimensional one. Consistency results for the methods are established under both fixed dimensions and diverging dimensions.
