Stationary Gaussian Markov Processes as Limits of Stationary Autoregressive Time Series
Loading...
Penn collection
Statistics Papers
Degree type
Discipline
Subject
continuous autoregressive processes
stationary Gaussian Markov processes
stochastic differential equations
Business
Mathematics
Partial Differential Equations
Statistics and Probability
stationary Gaussian Markov processes
stochastic differential equations
Business
Mathematics
Partial Differential Equations
Statistics and Probability
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Ernst, Philip A
Brown, Lawrence D
Shepp, Larry
Wolpert, Robert L
Contributor
Abstract
We consider the class, ℂp, of all zero mean stationary Gaussian processes, {Yt : t ∈ (—∞, ∞)} with p derivatives, for which the vector valued process {(Yt(0) ,...,Yt(p)) : t ≥ 0} is a p + 1-vector Markov process, where Yt(0) = Y(t). We provide a rigorous description and treatment of these stationary Gaussian processes as limits of stationary AR(p) time series.
Advisor
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Publication date
2017-03-01