We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian models with linear constraints on the covariance matrix. Maximum likelihood estimation for this ...

The predictive likelihood is useful for ranking models in forecast comparison exercises using Bayesian inference. We discuss how it can be estimated, by means of marginalization, for any subset of the ...

We propose an affine extension of the linear Gaussian term structure model (LGM) such that the instantaneous covariation of the factors is given by an affine process on semidefinite positive matrixes.

Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a ...

The generation of synthetic market data is widely seen as one of the most promising applications of sophisticated artificial intelligence models, such as generative adversarial networks (GANs) and ...