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Random coefficient minification processes

Author

Listed:
  • Lengyi Han

    (University of British Columbia)

  • W. John Braun

    (University of British Columbia)

  • Jason Loeppky

    (University of British Columbia)

Abstract

A common way to model nonnegative time series is to apply a log transformation and then use classical ARMA techniques. We demonstrate using Canadian Fire Weather Index (FWI) data that simulating from such models can lead to unrealistic data scenarios. Minification models provide another approach to nonnegative time series, but they can be too restrictive. We propose a random coefficient version of these processes, whose stationarity properties we study in this paper. This model has more flexibility than the fixed coefficient version of the process, and we demonstrate that simulated data from this model can be more realistic, and is so for the FWI series.

Suggested Citation

  • Lengyi Han & W. John Braun & Jason Loeppky, 2020. "Random coefficient minification processes," Statistical Papers, Springer, vol. 61(4), pages 1741-1762, August.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:4:d:10.1007_s00362-018-1000-6
    DOI: 10.1007/s00362-018-1000-6
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    References listed on IDEAS

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    1. D. Cifarelli & R. Gupta & K. Jayakumar, 2010. "On generalized semi-Pareto and semi-Burr distributions and random coefficient minification processes," Statistical Papers, Springer, vol. 51(1), pages 193-208, January.
    2. Miroslav Ristić, 2008. "A generalized semi-Pareto minification process," Statistical Papers, Springer, vol. 49(2), pages 343-351, April.
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    Cited by:

    1. Prakash, Shivendra & Markfort, Corey D., 2022. "A Monte-Carlo based 3-D ballistics model for guiding bat carcass surveys using environmental and turbine operational data," Ecological Modelling, Elsevier, vol. 470(C).

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