Accession Number : ADA181903
Title : Smoothness Priors in Time Series.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CA DEPT OF STATISTICS
Personal Author(s) : Gersch,Will ; Kitagawa,Genshiro
PDF Url : ADA181903
Report Date : 02 Jun 1987
Pagination or Media Count : 56
Abstract : A variety of time series signal extraction/smoothing problems are considered from a Bayesian smoothness priors point of view. The origin of the subject is a smoothing problem posed by Whittaker (1923). Using a stochastic regression-linear model-Gaussian disturbances framework, we model stationary time series and nonstationary mean and nonstationary covariance time series. Smoothness priors distributions on the model parameters are expressed either in terms of time domain stochastic difference equation or frequency domain constants. A small number of (hyper) parameters specify very complex time series behavior. The critical computation is the likelihood of the Bayesian model. Finally we show a smoothness priors state space - not necessarily Gaussian - not necessarily linear model of nonstationary time series.
Descriptors : *BAYES THEOREM, *TIME SERIES ANALYSIS, MATHEMATICAL MODELS, COMPUTATIONS, LINEAR SYSTEMS, STATIONARY, EXTRACTION, TIME SIGNALS, GAUSSIAN QUADRATURE, STOCHASTIC PROCESSES, REGRESSION ANALYSIS, MATRICES(MATHEMATICS)
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE