A bootstrap-based approach for parameter and polyspectral density estimation of a non-minimum phase ARMA process |
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Authors: | Shahnoor Shanta Visakan Kadirkamanathan |
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Affiliation: | 1. School of Electrical Engineering and Computer Science, University of Ottawa, Ontario, Canadasshanta@uottawa.ca;3. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK |
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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. |
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Keywords: | polyspectrum bispectrum dependent data bootstrap probability density estimation non-minimum phase system time reversal |
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