A New Autoregressive Time Series Model in Exponential Variables (NEAR(1)).

Abstract

A new time series model for exponential variables having first order autoregressive structure is presented. Unlike the recently studied standard autoregressive model in exponential variables (EAR(1)), runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of time nonreversibility effects in sample path behavior. The model is further developed by the use of cross-coupling and antithetic ideas to allow negative dependency. Joint distributions and autocorrelations are investigated. A transformed version of the model has a uniform marginal distribution and its correlation and regression structures are also obtained. Estimation aspects of the models are briefly considered. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1980
Accession Number
ADA101644

Entities

People

  • A. J. Lawrance
  • Peter A.W. Lewis

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Autocorrelation
  • California
  • Computational Science
  • Couplings
  • Data Science
  • Equations
  • Information Science
  • Joints
  • New York
  • Operations Research
  • Probability
  • Random Variables
  • Schools
  • Simulations
  • Standards
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.