BEST LINEAR UNBIASED ESTIMATION FOR MULTIVARIATE STATIONARY PROCESSES

Abstract

The general linear hypothesis is formulated for a multivariate stationary stochastic process. The best (minimum variance) linear unbiased estimates are derived for the regression functions and it is shown that many signal estimation problems are special cases of the general linear model. Several examples are presented illustrating the technique for particular multivariate processes. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 20, 1968
Accession Number
AD0828649

Entities

People

  • William C. Dean

Organizations

  • Teledyne Technologies

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Analysis Of Variance
  • Contracts
  • Covariance
  • Data Science
  • Fast Fourier Transforms
  • Frequency
  • Frequency Domain
  • Frequency Response
  • Information Processing
  • Information Science
  • Random Variables
  • Stationary Processes
  • Statistical Algorithms
  • Statistical Analysis
  • Stochastic Processes
  • Time Domain

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Mathematical Modeling and Probability Theory.