Modern Empirical Statistical Spectral Analysis.

Abstract

This paper has two aims: to provide perspectives on the diverse paths of analysis which are available in 1980 to estimate the spectrum of an observed time series; and to describe proposals for optimal statistical spectral estimation procedures which combine autoregressive spectral estimators and log spectral estimators. It is proposed that empirical statistical spectral analysis should be an adaptive procedure for forming an iterative spectral estimator (an iterative estimator is one composed of estimators obtained in different steps of the analysis). There are three parts: (I). Basic concepts of time series spectral analysis; (II). Entropy distances, autoregressive spectral estimators and log spectral estimators; (III). An outline of empirical spectral analysis. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1980
Accession Number
ADA084142

Entities

People

  • Emanuel Parzen

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computations
  • Data Analysis
  • Data Science
  • Equations
  • Estimators
  • Fast Fourier Transforms
  • Filters
  • Information Science
  • Mathematical Filters
  • Optimal Estimators
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Time Series Analysis
  • White Noise

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Statistical inference.