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stat.ME2019

Logistic regression models for aggregated data

Tom Whitaker, Boris Beranger, Scott A. Sisson

Logistic regression models are a popular and effective method to predict the probability of categorical response data. However inference for these models can become computationally…

stat.ME2019

High-dimensional inference using the extremal skew- process

B. Beranger, A. G. Stephenson, S. A. Sisson

Max-stable processes are a popular tool for the study of environmental extremes, and the extremal skew- process is a general model that allows for a flexible extremal dependence…

stat.ME2019

Estimation and uncertainty quantification for extreme quantile regions

Boris Beranger, Simone A. Padoan, Scott A. Sisson

Estimation of extreme quantile regions, spaces in which future extreme events can occur with a given low probability, even beyond the range of the observed data, is an important ta…

stat.ME2018

Extremal properties of the multivariate extended skew-normal distribution

Boris Beranger, Simone A. Padoan, Yangfan Xu +1

The skew-normal and related families are flexible and asymmetric parametric models suitable for modelling a diverse range of systems. We show that the multivariate maximum of a hig…

stat.ME2018

Extremal properties of the univariate extended skew-normal distribution

Boris Beranger, Simone A. Padoan, Yangfan Xu +1

We consider the extremal properties of the highly flexible univariate extended skew-normal distribution. We derive the well-known Mills' inequalities and Mills' ratio for the exten…