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: Rasaki, Olawale Olanrewaju, Anthony, Gichuhi Waititu, Nafiu, Lukman Abiodun
Format: Article
Published: American Journal of Mathematics and Statistics 2021
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Online Access:http://hdl.handle.net/20.500.12493/488
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author Rasaki, Olawale Olanrewaju
Anthony, Gichuhi Waititu
Nafiu, Lukman Abiodun
author_facet Rasaki, Olawale Olanrewaju
Anthony, Gichuhi Waititu
Nafiu, Lukman Abiodun
author_sort Rasaki, Olawale Olanrewaju
collection KAB-DR
description 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.
format Article
id oai:idr.kab.ac.ug:20.500.12493-488
institution KAB-DR
publishDate 2021
publisher American Journal of Mathematics and Statistics
record_format dspace
spelling oai:idr.kab.ac.ug:20.500.12493-4882024-01-17T04:43:43Z Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model Rasaki, Olawale Olanrewaju Anthony, Gichuhi Waititu Nafiu, Lukman Abiodun Fréchet distribution, Expectation-Maximization, Gnedenko-Fisher Tippet, k-regime-switching, Mixture Autoregressive, Multimodalities 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. Kabale University 2021-05-13T10:45:18Z 2021-05-13T10:45:18Z 2021 Article http://hdl.handle.net/20.500.12493/488 application/pdf American Journal of Mathematics and Statistics
spellingShingle Fréchet distribution, Expectation-Maximization, Gnedenko-Fisher Tippet, k-regime-switching, Mixture Autoregressive, Multimodalities
Rasaki, Olawale Olanrewaju
Anthony, Gichuhi Waititu
Nafiu, Lukman Abiodun
Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model
title Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model
title_full Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model
title_fullStr Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model
title_full_unstemmed Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model
title_short Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model
title_sort frechet random noise for k regime switching mixture autoregressive model
topic Fréchet distribution, Expectation-Maximization, Gnedenko-Fisher Tippet, k-regime-switching, Mixture Autoregressive, Multimodalities
url http://hdl.handle.net/20.500.12493/488
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AT anthonygichuhiwaititu frechetrandomnoiseforkregimeswitchingmixtureautoregressivemodel
AT nafiulukmanabiodun frechetrandomnoiseforkregimeswitchingmixtureautoregressivemodel