Identification of Nonlinear Times Series from First Order Cumulative Characteristics.

Abstract

We consider the problem of identifying the class of time series model to which a series belongs based on observation of part of the series. Techniques of nonparametric estimation have been applied to this problem by various authors using kernel estimates of the one-step lagged conditional mean and variance functions. We study cumulative versions of Tukey regressogram estimators of such functions. These are more stable than estimates of the mean and variance functions themselves and can be used to construct confidence bands. Goodness of fit tests for specific parametric models are also developed.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1991
Accession Number
ADA239822

Entities

People

  • Ian W. Mckeague
  • Mei-jie Zhang

Organizations

  • Florida State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Data Science
  • Ergodic Processes
  • Estimators
  • Goodness Of Fit Tests
  • Identification
  • Information Science
  • Markov Chains
  • Markov Processes
  • Military Research
  • Probability
  • Stationary
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Stochastic Processes
  • Surveys
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.