A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Modeling temporally uncorrelated components of complex-valued stationary processes
Tekijät: Lietzén Niko, Viitasaari Lauri, Ilmonen Pauliina
Kustantaja: VTEX
Julkaisuvuosi: 2021
Journal: Modern Stochastics: Theory and Applications
Tietokannassa oleva lehden nimi: MODERN STOCHASTICS-THEORY AND APPLICATIONS
Lehden akronyymi: MOD STOCH-THEORY APP
Vuosikerta: 8
Numero: 4
Aloitussivu: 475
Lopetussivu: 508
Sivujen määrä: 34
ISSN: 2351-6054
DOI: https://doi.org/10.15559/21-VMSTA190
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/68193907
A complex-valued linear mixture model is considered for discrete weakly stationary processes. Latent components of interest are recovered, which underwent a linear mixing. Asymptotic properties are studied of a classical unmixing estimator which is based on simultaneous diagonalization of the covariance matrix and an autocovariance matrix with lag tau. The main contributions are asymptotic results that can be applied to a large class of processes. In related literature, the processes are typically assumed to have weak correlations. This class is extended, and the unmixing estimator is considered under stronger dependency structures. In particular, the asymptotic behavior of the unmixing estimator is estimated for both long-and short-range dependent complex-valued processes. Consequently, this theory covers unmixing root T and unmixing estimators that produce non Gaussian asymptotic distributions. The presented methodology is a powerful preprocessing tool and highly applicable in several fields of statistics.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
