Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse ...

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211–50). We construct a Gibbs sampler algorithm to compute ...

Given the availability of large longitudinal data sets on human height and weight, different modelling approaches are at hand to access quantities of interest relating to important diagnostic aims.

Evaluating the impact of program activities on outcomes occasionally involves complex data structures – such as units nested within clusters, and observed longitudinally – and the corresponding ...