Optimal high-dimensional shrinkage covariance estimation for elliptical distributions

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

Kuvaus

We derive an optimal shrinkage sample covariance matrix (SCM) estimator which is suitable for high dimensional problems and when sampling from an unspecified elliptically symmetric distribution. Specifically, we derive the optimal (oracle) shrinkage parameters that obtain the minimum mean-squared error (MMSE) between the shrinkage SCM and the true covariance matrix when sampling from an elliptical distribution. Subsequently, we show how the oracle shrinkage parameters can be consistently estimated under the random matrix theory regime. Simulations show the advantage of the proposed estimator over the conventional shrinkage SCM estimator due to Ledoit and Wolf (2004). The proposed shrinkage SCM estimator often provides significantly better performance than the Ledoit-Wolf estimator and has the advantage that consistency is guaranteed over the whole class of elliptical distributions with finite 4th order moments.

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2017 25th European Signal Processing Conference (EUSIPCO)
TilaJulkaistu - lokakuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaEuropean Signal Processing Conference - Kos Island, Greece, Kos, Kreikka
Kesto: 28 elokuuta 20172 syyskuuta 2017
Konferenssinumero: 25
https://www.eusipco2017.org
https://www.eusipco2017.org/

Julkaisusarja

NimiEuropean Signal Processing Conference
ISSN (elektroninen)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
LyhennettäEUSIPCO
MaaKreikka
KaupunkiKos
Ajanjakso28/08/201702/09/2017
www-osoite

ID: 16859484