5,144 research outputs found
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
We propose a new variational Bayes estimator for high-dimensional copulas
with discrete, or a combination of discrete and continuous, margins. The method
is based on a variational approximation to a tractable augmented posterior, and
is faster than previous likelihood-based approaches. We use it to estimate
drawable vine copulas for univariate and multivariate Markov ordinal and mixed
time series. These have dimension , where is the number of observations
and is the number of series, and are difficult to estimate using previous
methods. The vine pair-copulas are carefully selected to allow for
heteroskedasticity, which is a feature of most ordinal time series data. When
combined with flexible margins, the resulting time series models also allow for
other common features of ordinal data, such as zero inflation, multiple modes
and under- or over-dispersion. Using six example series, we illustrate both the
flexibility of the time series copula models, and the efficacy of the
variational Bayes estimator for copulas of up to 792 dimensions and 60
parameters. This far exceeds the size and complexity of copula models for
discrete data that can be estimated using previous methods
Ensemble Copula Coupling as a Multivariate Discrete Copula Approach
In probability and statistics, copulas play important roles theoretically as
well as to address a wide range of problems in various application areas. In
this paper, we introduce the concept of multivariate discrete copulas, discuss
their equivalence to stochastic arrays, and provide a multivariate discrete
version of Sklar's theorem. These results provide the theoretical frame for the
ensemble copula coupling approach proposed by Schefzik et al. (2013) for the
multivariate statistical postprocessing of weather forecasts made by ensemble
systems.Comment: references correcte
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
Copulas and time series with long-ranged dependences
We review ideas on temporal dependences and recurrences in discrete time
series from several areas of natural and social sciences. We revisit existing
studies and redefine the relevant observables in the language of copulas (joint
laws of the ranks). We propose that copulas provide an appropriate mathematical
framework to study non-linear time dependences and related concepts - like
aftershocks, Omori law, recurrences, waiting times. We also critically argue
using this global approach that previous phenomenological attempts involving
only a long-ranged autocorrelation function lacked complexity in that they were
essentially mono-scale.Comment: 11 pages, 8 figure
Final solution to the problem of relating a true copula to an imprecise copula
In this paper we solve in the negative the problem proposed in this journal
(I. Montes et al., Sklar's theorem in an imprecise setting, Fuzzy Sets and
Systems, 278 (2015), 48-66) whether an order interval defined by an imprecise
copula contains a copula. Namely, if is a nonempty set of
copulas, then and are quasi-copulas and the pair
is an imprecise copula according to the
definition introduced in the cited paper, following the ideas of -boxes. We
show that there is an imprecise copula in this sense such that there is
no copula whatsoever satisfying . So, it is
questionable whether the proposed definition of the imprecise copula is in
accordance with the intentions of the initiators. Our methods may be of
independent interest: We upgrade the ideas of Dibala et al. (Defects and
transformations of quasi-copulas, Kybernetika, 52 (2016), 848-865) where
possibly negative volumes of quasi-copulas as defects from being copulas were
studied.Comment: 20 pages; added Conclusion, added some clarifications in proofs,
added some explanations at the beginning of each section, corrected typos,
results remain the sam
Factor copula models for item response data
Factor or conditional independence models based on copulas are proposed for multivariate discrete data such as item responses. The factor copula models have interpretations of latent maxima/minima (in comparison with latent means) and can lead to more probability in the joint upper or lower tail compared with factor models based on the discretized multivariate normal distribution (or multidimensional normal ogive model). Details on maximum likelihood estimation of parameters for the factor copula model are given, as well as analysis of the behavior of the log-likelihood. Our general methodology is illustrated with several item response data sets, and it is shown that there is a substantial improvement on existing models both conceptually and in fit to data
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