Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models

Abstract

The vector autoregressive moving average model is a multivariate stationary stochastic process where the unobservable multivariate process consists of independently identically distributed random vectors. The coefficient matrices and the covariance matrix are to be estimated from an observed sequence. Under the assumption of normality the method of maximum likelihood is applied to likelihoods suitably modified for techniques in the frequency and time domains. Newton-Raphson and scoring iterative methods are presented.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1978
Accession Number
ADA060867

Entities

People

  • Theodore W. Anderson

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Frequency
  • Frequency Domain
  • Information Science
  • Maximum Likelihood Estimation
  • Probability
  • Random Variables
  • Sequences
  • Stationary Processes
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Stochastic Processes
  • Surveys
  • Time Domain

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.