Dataset for: Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Spatio-Temporal Statistics and Data Analysis Group
Permanent link to this recordhttp://hdl.handle.net/10754/664504
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AbstractWhen performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
CitationYan, Y., Genton, M. G., & Admin, W. (2018). Dataset for: Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations. Figshare. https://doi.org/10.6084/M9.FIGSHARE.C.4075553.V1
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