Ordinary and Proper Location M-Estimates for ARMA Models. Revised.

Abstract

Proper location M-estimates for a model with non-Gaussian autoregressive-moving average type errors are genuine maximum likelihood type estimates, whereas ordinary location M-estimates are those introduced by P. Huber for independent and identically distributed errors. The relative behavior of ordinary location M-estimates and proper location M-estimates is studied for situations with dependent errors of purely autoregressive and purely moving average type. It is shown through asymptotic calculations and finite-sample size Monte Carlo studies that although ordinary location M-estimates are adequate for weak dependency structure, they can be quite inefficient compared with proper M-estimates of location when the non-Gaussian errors have a moderate to strong dependency structure. Additional keywords: Asymptotic normality; Efficiency; Equations. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1984
Accession Number
ADA149705

Entities

People

  • C. H. Lee
  • R. D. Martin

Organizations

  • University of Washington

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Asymptotic Normality
  • Coefficients
  • Computations
  • Data Analysis
  • Data Science
  • Efficiency
  • Gaussian Processes
  • Information Science
  • New York
  • Normal Distribution
  • Random Variables
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes
  • Universities

Fields of Study

  • Mathematics

Readers

  • Statistical inference.