Line mesh distributions: an alternative approach for multivariate environmental extremes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02

AUTHORS

Earl Bardsley, Varvara Vetrova, Ngoc Hieu Dao

ABSTRACT

Copulas and other multivariate models can give joint exceedance probabilities for multivariate events in the natural environment. However, the choice of the most appropriate multivariate model may not always be evident in the absence of knowledge of dependence structures. A simple nonparametric alternative is to approximate multivariate dependencies using “line mesh distributions”, introduced here as a data-based finite mixture of univariate distributions defined on a mesh of L = C(m, 2) lines extending through Euclidean n-space. That is, m data points in n-space define a total of L lines, where C() denotes the binomial coefficient. The utilitarian simplicity of the method has attraction for joint exceedance probabilities because just the data and a single bandwidth parameter within the 0, 1 interval are needed to define a line mesh distribution. All bivariate planes in these distributions have the same Pearson correlation coefficients as the corresponding data. Marginal means and variances are similarly preserved. Using an example from the literature, a 5-parameter bivariate Gumbel model is replaced with a 1-parameter line mesh distribution. A second illustration for three dimensions applies line mesh distributions to data simulated from a trivariate copula. More... »

PAGES

1-11

References to SciGraph publications

  • 2017-01. Multivariate flood risk analysis for Wei River in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2010-07. Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2016-08. Joint probability of precipitation and reservoir storage for drought estimation in the headwater basin of the Huaihe River, China in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2013-08. Copula-based risk evaluation of hydrological droughts in the East River basin, China in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2015-01. A nested multivariate copula approach to hydrometeorological simulations of spring floods: the case of the Richelieu River (Québec, Canada) record flood in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2014-03. Multivariate modeling of droughts using copulas and meta-heuristic methods in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
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