Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model
This paper describes Fréchet distribution as a random noise for capturing multimodalities, regime-switching and change-points attributed to uniformly time-varying series via causality of fluctuations, extreme values and heavy-tailed time series. Fréchet Mixture Autoregressive (FMAR) model of k-reg...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
American Journal of Mathematics and Statistics
2021
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12493/488 |
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| Summary: | This paper describes Fréchet distribution as a random noise for capturing multimodalities, regime-switching and
change-points attributed to uniformly time-varying series via causality of fluctuations, extreme values and heavy-tailed time
series. Fréchet Mixture Autoregressive (FMAR) model of k-regime-switching, denoted by FMAR(k; p1, p2 ,, pk ) was
developed and Expectation-Maximization (EM) algorithm was used as a method of parameter estimation for the embedded
coefficients of AR of k-mixing weights and lag pk. The limiting distribution of the FMAR(k; p1, p2 ,, pk ) model via
Gnedenko-Fisher Tippet limiting property was derived to asymptotically approach an exponential function. |
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