A non-Gaussian model for time series with pulses

Peter J. Diggle, Scott Zeger

Research output: Contribution to journalArticle

Abstract

A non-Gaussian autoregressive-like model is presented for time series that exhibit occasional large increases in value, termed pulses, and exponential decay between pulses. The model differs from a first-order autoregressive process in its incorporation of feedback between the distribution of the current innovation and the history of the process. Likelihood-based methods of inference for the model are developed, and an application to endocrinological data is given.

Original languageEnglish (US)
Pages (from-to)354-359
Number of pages6
JournalJournal of the American Statistical Association
Volume84
Issue number406
DOIs
StatePublished - 1989

Fingerprint

Time series
Autoregressive Process
Exponential Decay
Likelihood
Model
First-order
History
Innovation
Autoregressive process
Inference
Decay

Keywords

  • Autoregressive process
  • Feedback
  • Luteinizing hormone
  • Mixture distribution
  • Non-Gaussian time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

A non-Gaussian model for time series with pulses. / Diggle, Peter J.; Zeger, Scott.

In: Journal of the American Statistical Association, Vol. 84, No. 406, 1989, p. 354-359.

Research output: Contribution to journalArticle

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