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    Dataset for: Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

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    Type
    Dataset
    Authors
    Yan, Yuan
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/664504
    
    Metadata
    Show full item record
    Abstract
    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.V1
    Publisher
    figshare
    DOI
    10.6084/m9.figshare.c.4075553.v1
    Relations
    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
    Scopus Count
    Collections
    Datasets; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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