most citedAccounting for Skill in Trend, Variability, and Autocorrelation Facilitates Better Multi-Model Projections: Application to the AMOC and Temperature Time Series

14 citations

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2 papers

stat.AP2018★ 14 cited

Accounting for Skill in Trend, Variability, and Autocorrelation Facilitates Better Multi-Model Projections: Application to the AMOC and Temperature Time Series

Roman Olson, Soon-Il An, Yanan Fan +1

We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variabili…

stat.ME2016★ 11 cited

Exploratory data analysis for moderate extreme values using non-parametric kernel methods

Boris Beranger, Tarn Duong, Sarah E. Perkins-Kirkpatrick +1

In many settings it is critical to accurately model the extreme tail behaviour of a random process. Non-parametric density estimation methods are commonly implemented as explorator…