Dataset for: Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Type
DatasetAuthors
Yan, YuanGenton, Marc G.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionSpatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2018Permanent link to this record
http://hdl.handle.net/10754/664504
Metadata
Show full item recordAbstract
When 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.Citation
Yan, 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.V1Publisher
figshareRelations
Is Supplement To:- [Article]
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: 10.1002/env.2503 HANDLE: 10754/626528
ae974a485f413a2113503eed53cd6c53
10.6084/m9.figshare.c.4075553.v1