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We consider nonparametric estimation of a mixed discrete-continuous distribution under anisotropic smoothness … derive lower bounds on the estimation rates in the total variation distance. Next, we consider a nonparametric mixture of … normals models can be used for optimal adaptive estimation of mixed discretecontinuous distributions. …
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The definition of vectors of dependent random probability measures is a topic of interest in applications to Bayesian statistics. They, indeed, represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. In this...
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The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able...
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Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a …
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Financial contagion and systemic risk measures are commonly derived from conditional quantiles by using imposed model assumptions such as a linear parametrization. In this paper, we provide model free measures for contagion and systemic risk which are independent of the specifcation of...
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