Short item Published on November 30, 2015

Modelling big “N” spatiotemporal data

In modelling hierarchical data we can take into account spatial and temporal correlations by introducing spatiotemporal random effects in the model. Several other hurdles have to be overcome when modelling hierarchical mortality data, but Bayesian techniques with the aid of the Markov chain Monte Carlo (MCMC) simulation methods have successfully overcome these and fit spatiotemporal random effects for reasonably sized geo-locations. However, as the number of geo-locations increases, MCMC computations become infeasible or extremely slow, which is a norm in Big Data Analytics (BDA). This problem is popularly known as the “big m” or “big N”.