In this work, we propose a new framework for learning mixture models from continuous data. Gaussian Mixture Models (GMMs) are commonly used for this task and are popular among practitioners because of their sound statistical foundation and the availability of an efficient learning algorithm [2]. However, the underlying assumption about the normally distributed mixing components, is often too ri...

A novel flexible class of multivariate nonlinear non-Gaussian state space models, based on copulas, is proposed. Specifically, it assumed that the observation equation and are defined by copula families not necessarily equal. Inference performed within Bayesian framework, using Hamiltonian Monte Carlo method. Simulation studies show proposed copula-based approach extremely flexible, since able ...

Quantitative studies in many fields involve the analysis of multivariate data of diverse types, including measurements that we may consider binary, ordinal and continuous. One approach to the analysis of such mixed data is to use a copula model, in which the associations among the variables are parameterized separately from their univariate marginal distributions. The purpose of this article is...

Copulas offer economic agents facing uncertainty a powerful and flexible tool to model dependence between random variables and are preferable to the traditional, correlation-based approach. In this paper we show how standard tests for the fit of a distribution can be extended to copulas. Because they can be applied to any copula and because they are based on a direct comparison of a given copul...

Quantitative studies in many fields involve the analysis of multivariate data of diverse types, including measurements that we may consider binary, ordinal and continuous. One approach to the analysis of such mixed data is to use a copula model, in which the associations among the variables are parameterized separately from their univariate marginal distributions. The purpose of this article is...

Goodness-of-Fit Testing for Copula-based models with Applications in Atmospheric Science by Albert Rapp The University of Wisconsin–Milwaukee, 2017 Under the Supervision of Professor Vincent E. Larson Every elementary probability course discusses how to construct joint distribution functions of independent random variables but joint distribution functions of dependent random variables are usual...