Details

Multivariate Prediction, de Branges Spaces, and Related Extension and Inverse Problems

Operator Theory: Advances and Applications, Band 266

von: Damir Z. Arov, Harry Dym
106,99 €
Verlag:	Birkhäuser
Format:	PDF
Veröffentl.:	30.05.2018
ISBN/EAN:	9783319702629
Sprache:	englisch

In den Warenkorb

Als Gutschein

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

Titelbeschreibung

This monograph deals primarily with the prediction of vector valued stochastic processes that are either weakly stationary, or have weakly stationary increments, from finite segments of their past. The main focus is on the analytic counterpart of these problems, which amounts to computing projections onto subspaces of a Hilbert space of p x 1 vector valued functions with an inner product that is defined in terms of the p x p matrix valued spectral density of the process. The strategy is to identify these subspaces as vector valued de Branges spaces and then to express projections in terms of the reproducing kernels of these spaces and/or in terms of a generalized Fourier transform that is obtained from the solution of an associated inverse spectral problem. Subsequently, the projection of the past onto the future and the future onto the past is interpreted in terms of the range of appropriately defined Hankel operators and their adjoints, and, in the last chapter, assorted computations are carried out for rational spectral densities. The underlying mathematics needed to tackle this class of problems is developed in careful detail, but, to ease the reading, an attempt is made to avoid excessive generality. En route a number of results that, to the best of our knowledge, were only known for p = 1 are generalized to the case p > 1.

Inhaltsverzeichnis

Introduction.- Analytic preliminaries.- The de Branges spaces B(E) and H(A).- Three extension problems.- Spectral functions for ci problems.- Inverse spectral problems.- Generalizations.- Real and symmetric constraints.- Past and Future.- Conservative and passive systems.- Rational spectral densities.   

Autorenportrait

Back cover copy

This monograph deals primarily with the prediction of vector valued stochastic processes that are either weakly stationary, or have weakly stationary increments, from finite segments of their past. The main focus is on the analytic counterpart of these problems, which amounts to computing projections onto subspaces of a Hilbert space of p x 1 vector valued functions with an inner product that is defined in terms of the p x p matrix valued spectral density of the process. The strategy is to identify these subspaces as vector valued de Branges spaces and then to express projections in terms of the reproducing kernels of these spaces and/or in terms of a generalized Fourier transform that is obtained from the solution of an associated inverse spectral problem. Subsequently, the projection of the past onto the future and the future onto the past is interpreted in terms of the range of appropriately defined Hankel operators and their adjoints, and, in the last chapter,assorted computations are carried out for rational spectral densities. The underlying mathematics needed to tackle this class of problems is developed in careful detail, but, to ease the reading, an attempt is made to avoid excessive generality. En route a number of results that, to the best of our knowledge, were only known for p = 1 are generalized to the case p > 1.

Sonderbeitrag

Proposes solutions for prediction problems Develops the basics of Krein and de Branges theories in a new setting Makes the transition from one to several dimensions

Verkaufsargumente

Proposes solutions for prediction problems<div>Develops the basics of Krein and de Branges theories in a new setting</div><div>Makes the transition from one to several dimensions</div>