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The Backward Simulation (BS) approach was developed to generate, simply and efficiently, sample paths of correlated multivariate Poisson process with negative correlation coefficients between their components. In this paper, we extend the BS approach to model multivariate Mixed Poisson processes which have many important applications in Insurance, Finance, Geophysics and many other areas of Applied Probability. We also extend the Forward Continuation approach, introduced in our earlier work, to multivariate Mixed Poisson processes.
Multivariate GARCH models are important tools to describe the dynamics of multivariate times series of financial returns. Nevertheless, these models have been much less used in practice due to the lack of reliable software. This paper describes the {
We develop a fully Bayesian nonparametric regression model based on a Levy process prior named MLABS (Multivariate Levy Adaptive B-Spline regression) model, a multivariate version of the LARK (Levy Adaptive Regression Kernels) models, for estimating
We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence tests have R packages available, the interfaces are inconsistent and
The R package MfUSampler provides Monte Carlo Markov Chain machinery for generating samples from multivariate probability distributions using univariate sampling algorithms such as Slice Sampler and Adaptive Rejection Sampler. The sampler function pe
We propose a flexible model for count time series which has potential uses for both underdispersed and overdispersed data. The model is based on the Conway-Maxwell-Poisson (COM-Poisson) distribution with parameters varying along time to take serial c