Smoothness Priors Transfer Function Estimation.

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 the 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 Box-Jenkins series J data. Some of the statistical properties of the method are explored in Monte-Carlo simulation studies. Keywords: Bayesian model, Smoothness priors, Time series analysis, Transfer function estimation.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1987
Accession Number
ADA181607

Entities

People

  • Genshiro Kitagawa
  • Will Gersch

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Science
  • Computer Science
  • Equations
  • Frequency
  • Frequency Domain
  • Mathematics
  • Monte Carlo Method
  • Operations Research
  • Polynomials
  • Probability
  • Simulations
  • Statistics
  • Time Series Analysis
  • Transfer Functions
  • White Noise

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks