On A Spectral Estimate Obtained by an Autoregressive Model Fitting

Abstract

A stationary Gaussian process X(t) is considered which is expressed as an autoregressive process of infinite order. An autoregressive model of finite order K is fitted for this process and an estimate for the spectral density is obtained. The consistency and the asymptotic normality of this estimate under some conditions are shown. This estimate has an asymptotically efficient property in a sense under some conditions which are stronger than Berk's conditions.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1976
Accession Number
ADA025721

Entities

People

  • Mituaki Huzii

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Confidence Limits
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Maximum Likelihood Estimation
  • Military Research
  • Normal Distribution
  • Normality
  • Probability
  • Random Variables
  • Sequences
  • Stationary
  • Statistical Algorithms
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.