Goodness‐of‐fit tests for βARMA hydrological time series modeling
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DOI: 10.1002/env.2607
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Cited by:
- Cribari-Neto, Francisco & Scher, Vinícius T. & Bayer, Fábio M., 2023. "Beta autoregressive moving average model selection with application to modeling and forecasting stored hydroelectric energy," International Journal of Forecasting, Elsevier, vol. 39(1), pages 98-109.
- Palm, Bruna G. & Bayer, Fábio M. & Cintra, Renato J., 2022. "2-D Rayleigh autoregressive moving average model for SAR image modeling," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
- Melchior, Cristiane & Zanini, Roselaine Ruviaro & Guerra, Renata Rojas & Rockenbach, Dinei A., 2021. "Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches," International Journal of Forecasting, Elsevier, vol. 37(2), pages 825-837.
- Abraão D. C. Nascimento & Maria C. S. Lima & Hassan Bakouch & Najla Qarmalah, 2023. "Scaled Muth–ARMA Process Applied to Finance Market," Mathematics, MDPI, vol. 11(8), pages 1-18, April.
- Tingguo Zheng & Han Xiao & Rong Chen, 2022. "Generalized autoregressive moving average models with GARCH errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 125-146, January.
- Alice B. V. Mello & Maria C. S. Lima & Abraão D. C. Nascimento, 2022. "A notable Gamma‐Lindley first‐order autoregressive process: An application to hydrological data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
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