Score tests for inverse Gaussian mixtures

Tests of Fit for Normal Variance Inverse Gaussian Distributions

Goodness–of–fit tests for the family of symmetric normal variance inverse Gaussian distributions are constructed. The tests are based on a weighted integral incorporating the empirical characteristic function of suitably standardized data. An EM– type algorithm is employed for the estimation of the parameters involved in the test statistic. Monte Carlo results show that the new procedure is com...

Modeling localized bearing faults using inverse Gaussian mixtures

Localized bearing faults exhibit specific repetitive vibrational patterns. Due to the constant angular distance between the roller elements, the vibrational patterns occur on regular angular intervals. Under constant operating conditions such patterns become easily detectable as “periodic” events. Slippage or small variation in rotational speed are commonly modeled by introducing normally distr...

Hierarchical Gaussian Process Mixtures for

Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures

Transportation origin-destination analysis is investigated through the use of Poisson mixtures by introducing covariate-based models which incorporate different transport modelling phases and also allow for direct probabilistic inference on link traffic based on Bayesian predictions. Emphasis is placed on the Poisson-inverse Gaussian model as an alternative to the commonly used Poisson-gamma an...

Mixtures of Gaussian Processes

We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the optimal bandwidth of a map is input dependent. The MGP is derived from the mixture of experts model and can also be used for modeling general conditional probability densities. We discuss how Gaussian processes -in particular in form of Gaussian process classification, the support vector mac...