Autoregressive Spectral Estimation, Log Spectral Smoothing, and Entropy.

Abstract

Two important methods of spectral estimation, autoregressive spectral estimation, and log spectral kernel estimation are derived from a minimum information divergence estimation principle. The fact that autoregressive spectral estimators are maximum entropy estimators is shown to be proved without the use of the calculus of variations using the properties of minimum information divergence estimation. Adaptive procedures for forming these estimators (and combining to form iterated estimators) are provided by order-determining and truncation point determining criteria, which are described. An estimated spectrum is given for Wolfer's sunspot data. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1981
Accession Number
ADA104940

Entities

People

  • Emanuel Parzen

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Calculus Of Variations
  • Data Analysis
  • Data Science
  • Distribution Functions
  • Estimators
  • Filters
  • Information Science
  • Mathematical Filters
  • Maximum Likelihood Estimation
  • Multivariate Analysis
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Estimation
  • Statistics
  • Stochastic Processes
  • White Noise

Readers

  • Statistical inference.