Non-Gaussian autoregressive processes with Tukey g-and-h transformations

Yuan Yan, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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 (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.
Original languageEnglish (US)
Pages (from-to)e2503
JournalEnvironmetrics
Volume30
Issue number2
DOIs
StatePublished - May 23 2018

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