
Accession Number : ADA183836
Title : Smoothness Priors Transfer Function Estimation.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CA DEPT OF STATISTICS
Personal Author(s) : Gersch,Will ; Kitagawa,Genshiro
PDF Url : ADA183836
Report Date : 06 Aug 1987
Pagination or Media Count : 53
Abstract : A smoothness priors approach to transfer function estimation from stationary time series is shown. An infinite order impulse response model plus an infinite order additive AR noise model is assumed. This is algebraically equivalent to an infinite order ARMAX plus white noise model. A finite order ARMAX model approximation to this model is actually fitted to data. Frequency domain smoothness priors are assumed on the ARMAX polynomials and smoothness hyperparameters balance the tradeoff between the infidelity of the model to the data and the infidelity of model to the smoothness constraints. The likelihood of the hyperparameters is maximized by a least squares gradient search computational procedure. The method is illustrated by the analysis of the BoxJenkins series J data. Some of the statistical properties of the method are explored in Monte Carlo simulation studies. Keywords: Bayesian models; Linear regression models; Charts.
Descriptors : *ESTIMATES, *TRANSFER FUNCTIONS, MONTE CARLO METHOD, SIMULATION, POLYNOMIALS, STATISTICS, BAYES THEOREM, MATHEMATICAL MODELS, LINEAR REGRESSION ANALYSIS, MODELS, NOISE, STATIONARY, TIME SERIES ANALYSIS, TRADE OFF ANALYSIS, WHITE NOISE
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE