A significance test for classifying ARMA models

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.

Original languageEnglish
Pages (from-to)305-331
Number of pages27
JournalJournal of Statistical Computation and Simulation
Volume54
Issue number4
DOIs
Publication statusPublished - 1 Jan 1996

Keywords

  • ARMA models
  • Significance test
  • Time series

Cite this

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abstract = "Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.",
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}

A significance test for classifying ARMA models. / Maharaj, Elizabeth Ann.

In: Journal of Statistical Computation and Simulation, Vol. 54, No. 4, 01.01.1996, p. 305-331.

Research output: Contribution to journalArticleResearchpeer-review

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