Abstract
This chapter begins the more particular theory of stationary 2nd-order random processes, considered from the view-point of correlation theory. In other words, we will study processes which are “stationary in the wide sense” (page 7) and build a theory based on their covariance functions \({\rm K(s) = E(X}_{{\rm t + s}} \overline {\rm X} _{\rm t} )\) alone. This theory has the flavor of Hilbert space and Fourier analysis, and readers who are familiar with the “spectral theorem” for unitary operators on a Hilbert space will recognize that this theorem is behind the “spectral representation” of a stationary process to be derived below. No advance knowledge of spectral theory is needed, however, and in fact the probabilistic setting can provide an easy and well-motivated introduction to this area of functional analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1977 Springer-Verlag, New York Inc.
About this chapter
Cite this chapter
Lamperti, J. (1977). Stationary 2nd-Order Processes. In: Stochastic Processes. Applied Mathematical Sciences, vol 23. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-9358-0_3
Download citation
DOI: https://doi.org/10.1007/978-1-4684-9358-0_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-90275-3
Online ISBN: 978-1-4684-9358-0
eBook Packages: Springer Book Archive