Maximum Likelihood Estimation of the Autoregressive Coefficients and Moving Average Covariances of Vector Autoregressive Moving Average Models.

Abstract

The purpose of this paper is to derive asymptotically efficient estimates for the autoregressive matrix coefficients and moving average covariance matrices of the vector autoregressive moving average (VARMA) models in both time and frequency domains. To do this we shall apply the Newton-Raphson and scoring methods to the maximum likelihood equations derived from modified likelihood functions under the Gaussian Assumption.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1979
Accession Number
ADA073788

Entities

People

  • Fereydoon Ahrabi

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Coefficients
  • Computations
  • Covariance
  • Data Science
  • Equations
  • Frequency
  • Frequency Domain
  • Information Science
  • Maximum Likelihood Estimation
  • Normality
  • Observation
  • Probability
  • Random Variables
  • Stationary
  • Statistical Analysis
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.