2014/05/20 by G Marcon, Marcon, G., Simone A. Padoan +7 · 2 citations
Economics, Econometrics and Finance · Environmental Science · #Climate variability and models #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Hydrology and Drought Analysis #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1405.5228
openalex publication_date 2014/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many applications in risk analysis, especially in environmental sciences, require the estimation of the dependence among multivariate maxima. A way to do this is by inferring the Pickands dependence function of the underlying extreme-value copula. A nonparametric estimator is constructed as the sample equivalent of a multivariate extension of the madogram. Shape constraints on the family of Pickands dependence functions are taken into account by means of a representation in terms of a specific type of Bernstein polynomials. The large-sample theory of the estimator is developed and its finite-sample performance is evaluated with a simulation study. The approach is illustrated by analyzing clusters consisting of seven weather stations that have recorded weekly maxima of hourly rainfall in France from 1993 to 2011.