MilanPollution

Milan.winter

Milan.summer

<p>Two datasets, <code>Milan.summer</code> and <code>Milan.winter</code>, each containing
  5 air pollutants (daily maximum of NO2, NO, O3 and SO2, daily mean of PM10)
  and 6 meteorological covariates (maximum precipitation, maximum temperature,
  maximum humidity, mean precipitation, mean temperature and mean humidity).</p>

datasets

A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) <doi:10.48550/arXiv.1508.05561>, Marcon et al. (2016) <doi:10.1214/16-EJS1162>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Beranger et al. (2021) <doi:10.1007/s10687-019-00364-0>. This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) <doi:10.1007/s10687-020-00376-1>.

Simone Padoan

ExtremalDep

Extremal Dependence Models

Boris Beranger

Giulia Marcon

Steven G. Johnson

Rudolf Schuerer 

Brian Gough 

Alec G. Stephenson

Anne Sabourin

Philippe Naveau

MilanPollution function

<p>A \(1968 \times 12\) data frame and a
  \(1924 \times 12\) data frame.</p>

Format

Pollution data for summer and winter months in Milan, Italy — MilanPollution

Pollution data for summer and winter months in Milan, Italy

MilanPollution: Pollution data for summer and winter months in Milan, Italy

Description

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Format

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