RT Journal Article
T1 Strongly consistent autoregressive predictors in abstract Banach spaces
A1 Ruiz Medina, María Dolores
A1 Álvarez Liébana, Javier
A2 Von Rosen, Dietrich
AB This work derives new results on strong consistent estimation and prediction for autoregressive processes of order 1 in a separable Banach space B. The consistency results are obtained for the component-wise estimator of the autocorrelation operator in the norm of the space L(B) of bounded linear operators on B. The strong consistency of the associated plug-in predictor then follows in the B-norm. A Gelfand triple is defined through the Hilbert space constructed in Kuelbs’ lemma (Kuelbs, 1970). A Hilbert–Schmidt embedding introduces the Reproducing Kernel Hilbert space (RKHS), generated by the autocovariance operator, into the Hilbert space conforming the Rigged Hilbert space structure. This paper extends the work of Bosq (2000) and Labbas and Mourid (2002).
PB Elsevier
SN 0047-259X
YR 2018
FD 2018
LK https://hdl.handle.net/20.500.14352/95141
UL https://hdl.handle.net/20.500.14352/95141
LA eng
NO Ruiz-Medina, M. D.; Álvarez-Liébana, J. Strongly consistent autoregressive predictors in abstract Banach spaces. Journal of Multivariate Analysis 2019, 170, 186–201. doi:10.1016/j.jmva.2018.08.001.
NO Supplementary Material to "Strongly-consistent autoregressive predictors in abstract Banach spaces":https://ars-els-cdn-com.bucm.idm.oclc.org/content/image/1-s2.0-S0047259X17307248-mmc1.pdf
NO Ministerio de Economía, Comercio y Empresa (España)
DS Docta Complutense
RD 8 abr 2025