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dc.contributor.authorYan, Yuan
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2020-07-29T13:28:03Z
dc.date.available2020-07-29T13:28:03Z
dc.date.issued2018
dc.identifier.doi10.6084/m9.figshare.c.4075553.v1
dc.identifier.urihttp://hdl.handle.net/10754/664504
dc.description.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.
dc.publisherfigshare
dc.subjectEnvironmental Science
dc.titleDataset for: Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
dc.typeDataset
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics Program
kaust.personYan, Yuan
kaust.personGenton, Marc G.
dc.relation.issupplementtoDOI:10.1002/env.2503
display.relations<b> Is Supplement To:</b><br/> <ul> <li><i>[Article]</i> <br/> Yan Y, Genton MG (2018) Non-Gaussian autoregressive processes with Tukey g-and-h transformations. Environmetrics 30: e2503. Available: http://dx.doi.org/10.1002/env.2503.. DOI: <a href="https://doi.org/10.1002/env.2503" >10.1002/env.2503</a> HANDLE: <a href="http://hdl.handle.net/10754/626528">10754/626528</a></li></ul>


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