Maximum Likelihood Estimation of Multivariate Autoregressive-Moving Average Models.

Abstract

Algorithms for computing the exact likelihood function of n successive observation vectors from an s-variate autoregressive moving average process of order (p,q) are developed. A quasi-Newton method is used to maximize the likelihood function with respect to the parameters of the process. Monte Carlo simulations are performed to compare the parameter estimates obtained by maximizing the exact likelihood function versus those obtained by maximizing various approximate forms of the likelihood function. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1977
Accession Number
ADA038955

Entities

People

  • G. Kedem
  • M. S. Phadke

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Band Structures
  • Computations
  • Contracts
  • Data Science
  • Energy Bands
  • Frequency Domain
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Numbers
  • Observation
  • Probability
  • Simulations
  • Statistics
  • Time Domain

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Statistical inference.