Stationary Exponential Time Series: Further Model Development and a Residual Analysis.

Abstract

A second order autoregressive process in exponential variables, NEAR(2), is established: the distributional assumptions involved in this model highlight is a very broad four parameter structure which combines five exponential random variables into a sixth exponential random variable. The dependency structure of the NEAR(2) process beyond and including autocorrelations is explored using some new ideas on residual analysis for non-normal processes with autoregressive correlation structure. Other applications of the exponential structure are considered briefly. These include exponential time series with negative correlation and exponential time series with mixed autoregressive-moving average structure. An application to the analysis of a set of wind speed data is included. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1983
Accession Number
ADA128347

Entities

People

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

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Autocorrelation
  • California
  • Classification
  • Correlation Analysis
  • Cross Correlation
  • Data Analysis
  • Data Science
  • Equations
  • Information Science
  • Probability
  • Probability Distributions
  • Random Variables
  • Residuals
  • Schools
  • Stationary
  • Wind Velocity

Fields of Study

  • Mathematics

Readers

  • Statistical inference.