1,851 research outputs found
Weak convergence of the empirical copula process with respect to weighted metrics
The empirical copula process plays a central role in the asymptotic analysis
of many statistical procedures which are based on copulas or ranks. Among other
applications, results regarding its weak convergence can be used to develop
asymptotic theory for estimators of dependence measures or copula densities,
they allow to derive tests for stochastic independence or specific copula
structures, or they may serve as a fundamental tool for the analysis of
multivariate rank statistics. In the present paper, we establish weak
convergence of the empirical copula process (for observations that are allowed
to be serially dependent) with respect to weighted supremum distances. The
usefulness of our results is illustrated by applications to general bivariate
rank statistics and to estimation procedures for the Pickands dependence
function arising in multivariate extreme-value theory.Comment: 39 pages + 7 pages of supplementary material, 1 figur
Fusing Censored Dependent Data for Distributed Detection
In this paper, we consider a distributed detection problem for a censoring
sensor network where each sensor's communication rate is significantly reduced
by transmitting only "informative" observations to the Fusion Center (FC), and
censoring those deemed "uninformative". While the independence of data from
censoring sensors is often assumed in previous research, we explore spatial
dependence among observations. Our focus is on designing the fusion rule under
the Neyman-Pearson (NP) framework that takes into account the spatial
dependence among observations. Two transmission scenarios are considered, one
where uncensored observations are transmitted directly to the FC and second
where they are first quantized and then transmitted to further improve
transmission efficiency. Copula-based Generalized Likelihood Ratio Test (GLRT)
for censored data is proposed with both continuous and discrete messages
received at the FC corresponding to different transmission strategies. We
address the computational issues of the copula-based GLRTs involving
multidimensional integrals by presenting more efficient fusion rules, based on
the key idea of injecting controlled noise at the FC before fusion. Although,
the signal-to-noise ratio (SNR) is reduced by introducing controlled noise at
the receiver, simulation results demonstrate that the resulting noise-aided
fusion approach based on adding artificial noise performs very closely to the
exact copula-based GLRTs. Copula-based GLRTs and their noise-aided counterparts
by exploiting the spatial dependence greatly improve detection performance
compared with the fusion rule under independence assumption
Copulas in finance and insurance
Copulas provide a potential useful modeling tool to represent the dependence structure
among variables and to generate joint distributions by combining given marginal
distributions. Simulations play a relevant role in finance and insurance. They are used to
replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so
on. Using copulas, it is easy to construct and simulate from multivariate distributions based
on almost any choice of marginals and any type of dependence structure. In this paper we
outline recent contributions of statistical modeling using copulas in finance and insurance.
We review issues related to the notion of copulas, copula families, copula-based dynamic and
static dependence structure, copulas and latent factor models and simulation of copulas.
Finally, we outline hot topics in copulas with a special focus on model selection and
goodness-of-fit testing
Copula-based measurement of dependence between dimensions of well-being
Well-being consists of many dimensions such as income, health and education. A society exhibits greater dependence between its dimensions of well-being when the positions of the individuals in the different dimensions are more aligned or correlated. Differences in dependence may lead to very different societies, even when the dimension-wise distributions are identical. I propose to use a copula-based framework to order societies with respect to their dependence. A class of measures of dependence is derived to which the multidimensional rank correlation coefficient belongs. I illustrate the usefulness of the approach by showing that Russian dependence between three dimensions of well-being has increased significantly between 1995 and 2003. Unfortunately, the aspect of dependence is missed by all composite well-being measures based on dimension-specific summary statistics such as the popular Human Development Index (HDI).copula, complex inequality, concordance, HDI, multidimensional inequality, Russia, well-being.
Copula-based measurement of dependence between dimensions of well-being.
Well-being consists of many dimensions such as income, health and education. A society exhibits greater dependence between its dimensions of well-being when the positions of the individuals in the different dimensions are more aligned or correlated. Differences in dependence may lead to very different societies, even when the dimension-wise distributions are identical. I propose to use a copula-based framework to order societies with respect to their dependence. A class of measures of dependence is derived to which the multidimensional rank correlation coefficient belongs. I illustrate the usefulness of the approach by showing that Russian dependence between three dimensions of well-being has increased significantly between 1995 and 2003. Unfortunately, the aspect of dependence is missed by all composite well-being measures based on dimension-specific summary statistics such as the popular Human Development Index (HDI).
Copula-based orderings of multivariate dependence
In this paper I investigate the problem of defining a multivariate dependence ordering. First, I provide a characterization of the concordance dependence ordering between multivariate random vectors with fixed margins. Central to the characterization is a multivariate generalization of a well-known bivariate elementary dependence increasing rearrangement. Second, to order multivariate random vectors with non- fixed margins, I impose a scale invariance principle which leads to a copula-based concordance dependence ordering. Finally, a wide family of copula-based measures of dependence is characterized to which SpearmanĂs rank correlation coefficient belongs.copula, concordance ordering, dependence measures, dependence orderings, multivariate stochastic dominance, supermodular ordering
Nonparametric estimation of multivariate extreme-value copulas
Extreme-value copulas arise in the asymptotic theory for componentwise maxima
of independent random samples. An extreme-value copula is determined by its
Pickands dependence function, which is a function on the unit simplex subject
to certain shape constraints that arise from an integral transform of an
underlying measure called spectral measure. Multivariate extensions are
provided of certain rank-based nonparametric estimators of the Pickands
dependence function. The shape constraint that the estimator should itself be a
Pickands dependence function is enforced by replacing an initial estimator by
its best least-squares approximation in the set of Pickands dependence
functions having a discrete spectral measure supported on a sufficiently fine
grid. Weak convergence of the standardized estimators is demonstrated and the
finite-sample performance of the estimators is investigated by means of a
simulation experiment.Comment: 26 pages; submitted; Universit\'e catholique de Louvain, Institut de
statistique, biostatistique et sciences actuarielle
Extremal attractors of Liouville copulas
Liouville copulas, which were introduced in McNeil and Neslehova (2010), are
asymmetric generalizations of the ubiquitous Archimedean copula class. They are
the dependence structures of scale mixtures of Dirichlet distributions, also
called Liouville distributions. In this paper, the limiting extreme-value
copulas of Liouville copulas and of their survival counterparts are derived.
The limiting max-stable models, termed here the scaled extremal Dirichlet, are
new and encompass several existing classes of multivariate max-stable
distributions, including the logistic, negative logistic and extremal
Dirichlet. As shown herein, the stable tail dependence function and angular
density of the scaled extremal Dirichlet model have a tractable form, which in
turn leads to a simple de Haan representation. The latter is used to design
efficient algorithms for unconditional simulation based on the work of Dombry,
Engelke and Oesting (2015) and to derive tractable formulas for
maximum-likelihood inference. The scaled extremal Dirichlet model is
illustrated on river flow data of the river Isar in southern Germany.Comment: 30 pages including supplementary material, 6 figure
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