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Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask

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TLDR
This paper presents an introduction to inference for copula models, based on rank methods, by working out in detail a small, fictitious numerical example, the various steps involved in investigating the dependence between two random variables and in modeling it using copulas.
Abstract
This paper presents an introduction to inference for copula models, based on rank methods. By working out in detail a small, fictitious numerical example, the writers exhibit the various steps involved in investigating the dependence between two random variables and in modeling it using copulas. Simple graphical tools and numerical techniques are presented for selecting an appropriate model, estimating its parameters, and checking its goodness-of-fit. A larger, realistic application of the methodology to hydrological data is then presented.

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Citations
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Journal ArticleDOI

Joint modelling of flood peaks and volumes: A copula application for the Danube River

TL;DR: In this paper, the suitability of various copula families for a bivariate analysis of peak discharges and flood volumes has been tested using streamflow data from selected gauging stations along the whole Danube River.
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Joint probability analysis of extreme wave heights and surges along China's coasts

TL;DR: In this paper, the joint probability of extreme wave height and surge at 9 representatively selected stations along China's coasts is analyzed using the results extracted from long-term model simulations over a period of 35 years.
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Modelling dependence of extreme events in energy markets using tail copulas

TL;DR: In this paper, the dependence of extreme events in energy markets was studied for the first time, based on a large data set comprising quotes of crude oil and natural gas futures, and the authors estimate and model large co-movements of commodity returns.
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A Copula Model for Non-Gaussian Multivariate Spatial Data

TL;DR: In this paper, the authors proposed a new copula model for replicated multivariate spatial data, which is based on the assumption that some factors exist that affect the joint spatial dependence of all measurements of each variable as well as the joint dependence among these variables.
References
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Book

An Introduction to Copulas

TL;DR: This book discusses the fundamental properties of copulas and some of their primary applications, which include the study of dependence and measures of association, and the construction of families of bivariate distributions.
Journal ArticleDOI

Multivariate models and dependence concepts

Harry Joe
- 01 Sep 1998 - 
TL;DR: Introduction.
Journal ArticleDOI

Non-Uniform Random Variate Generation.

B. J. T. Morgan, +1 more
- 01 Sep 1988 - 
TL;DR: This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
Book ChapterDOI

A Class of Statistics with Asymptotically Normal Distribution

TL;DR: In this article, the authors considered the problem of estimating a U-statistic of the population characteristic of a regular functional function, where the sum ∑″ is extended over all permutations (α 1, α m ) of different integers, 1 α≤ (αi≤ n, n).