Leiva, V, Saulo, H, Souza, R et al. (2 more authors) (2021) A new BISARMA time series model for forecasting mortality using weather and particulate matter data. Journal of Forecasting, 40 (2). pp. 346-364. ISSN 0277-6693
Abstract
The Birnbaum–Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of this distribution and the development of many articles, few of them have considered data with a dependency structure. To fill this gap, we introduce a new class of time series models based on the BS distribution, which allows modeling of positive and asymmetric data that have an autoregressive structure. We call these BS autoregressive moving average (BISARMA) models. Also included is a thorough study of theoretical properties of the proposed methodology and of practical issues, such as maximum likelihood parameter estimation, diagnostic analytics, and prediction. The performance of the proposed methodology is evaluated using Monte Carlo simulations. An analysis of real‐world data is performed using the methodology to show its potential for applications. The numerical results report the excellent performance of the BISARMA model, indicating that the BS distribution is a good modeling choice when dealing with time series data with positive support and asymmetrically distributed. Hence, it can be a valuable addition to the toolkit of applied statisticians and data scientists.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 John Wiley & Sons, Ltd. This is the peer reviewed version of the following article: Leiva, V, Saulo, H, Souza, R et al. (2 more authors) (2021) A new BISARMA time series model for forecasting mortality using weather and particulate matter data. Journal of Forecasting, 40 (2). pp. 346-364. ISSN 0277-6693 , which has been published in final form at http://doi.org/10.1002/for.2718. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. |
Keywords: | ARMA models; Birnbaum–Saunders distribution; data dependent over time; maximum likelihood and Monte Carlo methods; model selection; residuals; R software |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jun 2020 11:54 |
Last Modified: | 24 Jun 2022 00:13 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/for.2718 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162235 |