Efficient Estimation of a Model with an Autoregressive Signal with White Noise.

Abstract

This paper considers the estimation of parameters in the model of X sub t = S sub t + epsilon sub t where the S sub t are generated by a stationary autoregressive model sum from i = o to p of (alpha sub i) (S sub t-i) = eta sub t and the eta sub t and the epsilon sub t are i.i.d. random variables. This paper gives the asymptotic distribution of an approximate maximum-likelihood estimate using only a condition on the fourth-order moment of epsilon sub t and eta sub t and without the assumption of normality. This paper also contains a theorem which shows that under general conditions an estimate given by the second-step in the Newton-Raphson iteration with a consistent estimate as an initial value is second-order efficient.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1979
Accession Number
ADA069571

Entities

People

  • Yuzo Hosoya

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Contracts
  • Data Science
  • Information Science
  • Iterations
  • Maximum Likelihood Estimation
  • Military Research
  • New York
  • Noise
  • Normality
  • Random Variables
  • Stationary Processes
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Stochastic Processes
  • United States
  • White Noise

Fields of Study

  • Mathematics

Readers

  • Analytical Mechanics
  • Statistical inference.