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A bootstrap-based approach for parameter and polyspectral density estimation of a non-minimum phase ARMA process

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  • Shahnoor Shanta
  • Visakan Kadirkamanathan

Abstract

A bootstrap-based methodology is developed for parameter estimation and polyspectral density estimation in the case of the approximating model of the underlying stochastic process being non-minimum phase autoregressive-moving-average (ARMA) type, given a finite realisation of a single time series data. The method is based on a minimum phase/maximum phase decomposition of the system function together with a time reversal step for the parameter and polyspectral confidence interval estimation. Simulation examples are provided to illustrate the proposed method.

Suggested Citation

  • Shahnoor Shanta & Visakan Kadirkamanathan, 2015. "A bootstrap-based approach for parameter and polyspectral density estimation of a non-minimum phase ARMA process," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(3), pages 418-428, February.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:3:p:418-428
    DOI: 10.1080/00207721.2013.784444
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    Cited by:

    1. Quansen Wang & Jianzhong Zhou & Kangdi Huang & Ling Dai & Benjun Jia & Lu Chen & Hui Qin, 2021. "A Procedure for Combining Improved Correlated Sampling Methods and a Resampling Strategy to Generate a Multi-Site Conditioned Streamflow Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1011-1027, February.

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