On the Rate of Mean Convergence of Finite Linear Predictors of Multivariate Stationary Stochastic Processes.

Abstract

This document considers a multivariate weakly stationary stochastic process (X sub n) with the spectral density matrix f satisfying the boundedness condition. It is shown that if the entries of f are analytic functions of theta on -pi,pi, then the rate of convergence of the one-step ahead linear least squares predictor of (X sub n) based on a finite segment of the past, and the partial sum of the infinite linear least squares predictor of the process to the Kolmogorov-Wiener predictor is at least exponential. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1984
Accession Number
ADA145609

Entities

People

  • M. Pourahmadi

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Analytic Functions
  • Convergence
  • Data Science
  • Fourier Series
  • Hilbert Space
  • Information Science
  • North Carolina
  • Probability
  • Random Variables
  • Stationary
  • Stationary Processes
  • Statistics
  • Stochastic Processes
  • Two Dimensional
  • Universities

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Psychometric Testing or Psychological Assessment.
  • Statistical inference.