Non-Gaussian autoregressive processes with Tukey g-and-h transformations
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2640
Preprint Posting Date2017-11-20
Online Publication Date2018-05-23
Print Publication Date2019-03
Permanent link to this recordhttp://hdl.handle.net/10754/626528
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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 (Formula presented.) -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 MG (2018) Non-Gaussian autoregressive processes with Tukey g-and-h transformations. Environmetrics 30: e2503. Available: http://dx.doi.org/10.1002/env.2503.
SponsorsThis publication is based upon the work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Grant OSR-2015-CRG4-2640.
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