Maximum Likelihood Estimation of the Covariances of the Vector Moving Average Models in the Time and Frequency Domains

Abstract

The vector moving average process is a stationary stochastic process, where the unobservable process consists of independently identically distributed random variables. The matrix parameters are estimated from the observations. The likelihood function is derived under normality and to solve the maximum likelihood equations the Newton-Raphson and Scoring methods are used. The estimation problem is considered in the time and frequency domains. Asymptotic efficiency of the estimates is established.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1978
Accession Number
ADA060820

Entities

People

  • F. Ahrabi

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

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

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.