Time Series Long Memory Identification and Quantile Spectral Analysis.

Abstract

An approach to spectral estimation is described which involves the simultaneous use of frequency, time, and quantile domain algorithms, and is called quantile spectral analysis. It is based on the premise that while the spectrum is a non-parametric concept, its estimation cannot be a non-parametric procedure to be conducted independently of model identification. We discuss: the goals of spectral analysis, quantile data analysis, identification of memory (no, short, long), index of regular variation of a spectral density, autoregressive spectral estimation, and ARMA model identification by estimating MA (infinity) and subset regression. An illustrative example is given of quantile spectral analysis. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1983
Accession Number
ADA132240

Entities

People

  • Emanuel Parzen

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Data Analysis
  • Data Science
  • Distribution Functions
  • Dynamic Range
  • Estimators
  • Frequency
  • Information Science
  • Military Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Data
  • Statistics
  • Time Series Analysis
  • White Noise

Readers

  • Statistical inference.