Infinite Order Autoregressive Representations of Multivariate Stationary Stochastic Processes.

Abstract

While it is well-known that every purely nondeterministic full rank q- variate weakly stationary stochastic process (X sub n) with the spectral density W has an (infinite order) one-sided moving average representation, not every such process can have a mean convergent (infinite order) autoregressive representation (ARR) and the problem of ARR of such processes has not received the attention which it deserves. Due to the importance of ARR in prediction theory, and particularly in the statistical theory of multivariate time series, this paper is devoted to the problem of finding the weakest condition on W which guarantees the existence of an infinite order ARR for (X sub n).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1984
Accession Number
ADA147863

Entities

People

  • M. Pourahmadi

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Classification
  • Coefficients
  • Data Science
  • Fourier Series
  • Hilbert Space
  • Infinite Series
  • Information Science
  • North Carolina
  • Probability
  • Random Variables
  • Sequences
  • Stationary
  • Stationary Processes
  • Statistics
  • Stochastic Processes
  • Time Domain
  • Universities

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.