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Fitting Non-Gaussian Time Series Models

  • Conference paper
COMPSTAT

Abstract

We consider a class of Generalized Autoregressive Moving Average (GARMA) models which extend the univariate Gaussian ARMA time series model to a flexible model for non-Gaussian time series data. Estimation of the model is carried out using an iteratively reweighted least squares algorithm. The model is demonstrated by its application to a time series data set.

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Author information

Authors and Affiliations

  1. STORM Research Centre, University of North London, London, N7 8DB, UK

    Michael A. Benjamin, Robert A. Rigby & Mikis D. Stasinopoulos

Authors
  1. Michael A. Benjamin
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  2. Robert A. Rigby
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  3. Mikis D. Stasinopoulos
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Editor information

Editors and Affiliations

  1. Statistics Department, IACR-Rothamsted, AL5 2JQ, Harpenden, Herts, UK

    Roger Payne

  2. Department of Mathematics, University of Bristol, BS8 1TW, Bristol, UK

    Peter Green

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© 1998 Springer-Verlag Berlin Heidelberg

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Benjamin, M.A., Rigby, R.A., Stasinopoulos, M.D. (1998). Fitting Non-Gaussian Time Series Models. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_20

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  • DOI: https://doi.org/10.1007/978-3-662-01131-7_20

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1131-5

  • Online ISBN: 978-3-662-01131-7

  • eBook Packages: Springer Book Archive

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