65 citations · 232 across the 16 of their papers we have counts for
18 papers
A Vector Space Approach to Heavy Tailed Analysis
Kenneth Broadhead, Daniel Cooley
We construct a vector space whose defining characteristics are rooted in univariate regular variation of random variables. Specifically, the base vector space consis…
Quantifying Very Extreme Precipitation and Temperature Using Huge Ensembles Generated by Machine Learning-based Climate Model Emulators
Christopher J. Paciorek, Daniel Cooley
Weather extremes produce major impacts on society and ecosystems and are likely to change in likelihood and magnitude with climate change. However, very low probability events are…
Transformed-Linear Innovations Algorithm for Modeling and Forecasting of Time Series Extremes
Nehali Mhatre, Daniel Cooley
The innovations algorithm is a classical recursive forecasting algorithm used in time series analysis. We develop the innovations algorithm for a class of nonnegative regularly var…
Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud
Reetam Majumder, Benjamin A. Shaby, Brian J. Reich +1
We propose a model to flexibly estimate joint tail properties by exploiting the convergence of an appropriately scaled point cloud onto a compact limit set. Characteristics of the…
Transformed Linear Prediction for Extremes
Jeongjin Lee, Daniel Cooley
We address the problem of prediction for extreme observations by proposing an extremal linear prediction method. We construct an inner product space of nonnegative random variables…
Transformed-Linear Models for Time Series Extremes
Nehali Mhatre, Daniel Cooley
In order to capture the dependence in the upper tail of a time series, we develop non-negative regularly-varying time series models that are constructed similarly to classical non-…