1,768 research outputs found
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification task
but must learn from different data sets. The learners cannot share data but
instead share their models. Models are shared only one time so as to preserve
the network load. We introduce DELCO (standing for Decentralized Ensemble
Learning with COpulas), a new approach allowing to aggregate the predictions of
the classifiers trained by each learner. The proposed method aggregates the
base classifiers using a probabilistic model relying on Gaussian copulas.
Experiments on logistic regressor ensembles demonstrate competing accuracy and
increased robustness in case of dependent classifiers. A companion python
implementation can be downloaded at https://github.com/john-klein/DELC
copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas
The use of copula-based models in EDAs (estimation of distribution
algorithms) is currently an active area of research. In this context, the
copulaedas package for R provides a platform where EDAs based on copulas can be
implemented and studied. The package offers complete implementations of various
EDAs based on copulas and vines, a group of well-known optimization problems,
and utility functions to study the performance of the algorithms. Newly
developed EDAs can be easily integrated into the package by extending an S4
class with generic functions for their main components. This paper presents
copulaedas by providing an overview of EDAs based on copulas, a description of
the implementation of the package, and an illustration of its use through
examples. The examples include running the EDAs defined in the package,
implementing new algorithms, and performing an empirical study to compare the
behavior of different algorithms on benchmark functions and a real-world
problem
Nested Archimedean Copulas Meet R: The nacopula Package
The package nacopula provides procedures for constructing nested Archimedean copulas in any dimensions and with any kind of nesting structure, generating vectors of random variates from the constructed objects, computing function values and probabilities of falling into hypercubes, as well as evaluation of characteristics such as Kendall's tau and the tail-dependence coefficients. As by-products, algorithms for various distributions, including exponentially tilted stable and Sibuya distributions, are implemented. Detailed examples are given.
Mixed Marginal Copula Modeling
This article extends the literature on copulas with discrete or continuous
marginals to the case where some of the marginals are a mixture of discrete and
continuous components. We do so by carefully defining the likelihood as the
density of the observations with respect to a mixed measure. The treatment is
quite general, although we focus focus on mixtures of Gaussian and Archimedean
copulas. The inference is Bayesian with the estimation carried out by Markov
chain Monte Carlo. We illustrate the methodology and algorithms by applying
them to estimate a multivariate income dynamics model.Comment: 46 pages, 8 tables and 4 figure
Testing the Gaussian Copula Hypothesis for Financial Assets Dependences
Using one of the key property of copulas that they remain invariant under an
arbitrary monotonous change of variable, we investigate the null hypothesis
that the dependence between financial assets can be modeled by the Gaussian
copula. We find that most pairs of currencies and pairs of major stocks are
compatible with the Gaussian copula hypothesis, while this hypothesis can be
rejected for the dependence between pairs of commodities (metals).
Notwithstanding the apparent qualification of the Gaussian copula hypothesis
for most of the currencies and the stocks, a non-Gaussian copula, such as the
Student's copula, cannot be rejected if it has sufficiently many ``degrees of
freedom''. As a consequence, it may be very dangerous to embrace blindly the
Gaussian copula hypothesis, especially when the correlation coefficient between
the pair of asset is too high as the tail dependence neglected by the Gaussian
copula can be as large as 0.6, i.e., three out five extreme events which occur
in unison are missed.Comment: Latex document of 43 pages including 14 eps figure
Generating VaR scenarios with product beta distributions
We propose a Monte Carlo simulation method to generate stress tests by VaR
scenarios under Solvency II for dependent risks on the basis of observed data.
This is of particular interest for the construction of Internal Models and
requirements on evaluation processes formulated in the Commission Delegated
Regulation. The approach is based on former work on partition-ofunity copulas,
however with a direct scenario estimation of the joint density by product beta
distributions after a suitable transformation of the original data.Comment: 10 pages, 25 figures, 5 table
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