Time Series Models with a Specified Symmetric Non-Normal Marginal Distribution.

Abstract

Time series models with autoregressive, moving average and mixed autoregressive-moving averager correlation structure and with symmetric, heavy-tailed, non-normal marginal distributions, called (letter)-Laplace, are considered. First, a flexible mixed model NLARMA(p,q) with Laplace (double exponential) marginals is investigated. Second, a family of continuous random coefficient models with l-Laplace distributions are examined. The Laplace distribution is described along with a useful transformation. Thirdly, the NLAR(1) and the BELAR(1) processes are compared using higher order residual analyses based on the uncorrelated, but dependent linear residuals, (R sub n). Finally, open problems, as well as possible extensions and applications of the analyses given in this thesis are discussed. Keywords: Maximum Likelihood estimation; Least squares method; Residual analysis.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1985
Accession Number
ADA162262

Entities

People

  • Lee S. Dewald Sr

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computational Science
  • Data Science
  • Estimators
  • Information Processing
  • Information Science
  • Knowledge Management
  • Mathematics
  • Maximum Likelihood Estimation
  • Operations Research
  • Plastic Explosives
  • Random Variables
  • Sequences
  • Simulations
  • Statistical Algorithms
  • Statistics
  • Two Dimensional
  • United States Military Academy

Fields of Study

  • Mathematics

Readers

  • Statistical inference.