Quantification of the environmental structural risk with spoiling ties: is randomization worthwhile? View Full Text


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Article Info

DATE

2016-11-23

AUTHORS

R. Pappadà, F. Durante, G. Salvadori

ABSTRACT

Many recent works show that copulas turn out to be useful in a variety of different applications, especially in environmental sciences. Here the variables of interest are usually continuous, being times, lengths, weights, and so on. Unfortunately, the corresponding observations may suffer from (instrumental) adjustments and truncations, and eventually may show several repeated values (i.e., ties). In turn, on the one hand, a tricky issue of identifiability of the model arises, and, on the other hand, the assessment of the risk may be adversely affected. A possible remedy is to adopt suitable randomization procedures: here three different strategies are outlined. The goal of the work is to carry out a simulation study in order to evaluate the effects of the randomization of multivariate observations when ties are present. In particular, it is investigated whether, how, and to what extent, the randomization may change the estimation of the structural risk: for this purpose, a coastal engineering example will be used, as archetypical of a broad class of models and problems in engineering applications. Practical advices and warnings about the use of randomization techniques are hence given. More... »

PAGES

2483-2497

References to SciGraph publications

  • 2009-07-14. A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems in STATISTICS AND COMPUTING
  • 2011-06. Discussion: Statistical models and methods for dependence in insurance data in JOURNAL OF THE KOREAN STATISTICAL SOCIETY
  • 2007. Extremes in Nature, An Approach Using Copulas in NONE
  • 2014. Engineering Risk Assessment in RISK - A MULTIDISCIPLINARY INTRODUCTION
  • 2015-06-28. Spin-off Extreme Value and Archimedean copulas for estimating the bivariate structural risk in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
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    http://scigraph.springernature.com/pub.10.1007/s00477-016-1357-9

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    http://dx.doi.org/10.1007/s00477-016-1357-9

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