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November 2001 Reproducing Kernel Hilbert Space Methods for wide-sense self-similar Processes
Carl J. Nuzman, H. Vincent Poor
Ann. Appl. Probab. 11(4): 1199-1219 (November 2001). DOI: 10.1214/aoap/1015345400

Abstract

It has recently been observed that wide-sense self-similar processes have a rich linear structure analogous to that of wide-sense stationary processes. In this paper, a reproducing kernel Hilbert space (RKHS) approach is used to characterize this structure. The RKHS associated with a self-similar process on a variety of simple index sets has a straightforward description, provided that the scale-spectrum of the process can be factored. This RKHS description makes use of the Mellin transform and linear self-similar systems in much the same way that Laplace transforms and linear time-invariant systems are used to study stationary processes.

The RKHS results are applied to solve linear problems including projection, polynomial signal detection and polynomial amplitude estimation, for general wide-sense self-similar processes. These solutions are applied specifically to fractional Brownian motion (fBm). Minimum variance unbiased estimators are given for the amplitudes of polynomial trends in fBm, and two new innovations representations for fBm are presented.

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Carl J. Nuzman. H. Vincent Poor. "Reproducing Kernel Hilbert Space Methods for wide-sense self-similar Processes." Ann. Appl. Probab. 11 (4) 1199 - 1219, November 2001. https://doi.org/10.1214/aoap/1015345400

Information

Published: November 2001
First available in Project Euclid: 5 March 2002

zbMATH: 1012.60043
MathSciNet: MR1878295
Digital Object Identifier: 10.1214/aoap/1015345400

Subjects:
Primary: 60G18
Secondary: 46E22 , 60G35

Keywords: Detection , estimation , fractional Brownian motion , innovations , Lamperti’s transformation , Mellin transform , ‎reproducing kernel Hilbert ‎space , Self-similar

Rights: Copyright © 2001 Institute of Mathematical Statistics

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Vol.11 • No. 4 • November 2001
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