Adaptive Lasso based on joint M-estimation of regression and scale

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

Kuvaus

The adaptive Lasso (Least Absolute Shrinkage and Selection Operator) obtains oracle variable selection property by using cleverly chosen adaptive weights for regression coefficients in the 1-penalty. In this paper, in the spirit of M-estimation of regression, we propose a class of adaptive M-Lasso estimates of regression and scale as solutions to generalized zero subgradient equations. The defining estimating equations depend on a differentiable convex loss function and choosing the LS-loss function yields the standard adaptive Lasso estimate and the associated scale statistic. An efficient algorithm, a generalization of the cyclic coordinate descent algorithm, is developed for computing the proposed M-Lasso estimates. We also propose adaptive MLasso estimate of regression with preliminary scale estimate that uses a highly-robust bounded loss function. A unique feature of the paper is that we consider complex-valued measurements and regression parameter. Consistent variable selection property of the adaptive M-Lasso estimates are illustrated with a simulation study.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 24th European Signal Processing Conference, EUSIPCO 2016
TilaJulkaistu - 28 marraskuuta 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaEuropean Signal Processing Conference - Budapest, Unkari
Kesto: 28 elokuuta 20162 syyskuuta 2016
Konferenssinumero: 24
http://www.eusipco2016.org/

Julkaisusarja

NimiEuropean Signal Processing Conference
KustantajaInstitute of Electrical and Electronics Engineers, Inc.
ISSN (painettu)2219-5491
ISSN (elektroninen)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
LyhennettäEUSIPCO
MaaUnkari
KaupunkiBudapest
Ajanjakso28/08/201602/09/2016
www-osoite

ID: 10193602