Spatial statistics and computational methods

TL;DR: The objective of the study was to explore some of the issues raised in recent roadway safety studies regarding ranking methodologies in light of the recent statistical development in space-time GLMM.

TL;DR: A statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data.

TL;DR: A Markov chain Monte Carlo algorithm and a new post-processing scheme are proposed that significantly improve the accuracy of previously existing algorithms in terms of estimated number of populations and estimated population membership.

TL;DR: The map provided an initial description of the geographic variation of malaria risk in Malawi, and might help in the choice and design of interventions, which is crucial for reducing the burden of malaria.

TL;DR: The authors consider applications tofolio optimization and actuarial risks (investigation of the dependencies between individual claims on the riskiness of portfolios), and their eye is always toward application.

TL;DR: This chapter is the first of a series of two on simulation methods based on Markov chains, where the Metropolis–Hastings algorithm can be seen as one of the most general Markov chain Monte (MCMC) algorithms.

TL;DR: Markov Random Fields (MRFs) as mentioned in this paper are a general mathematical construct that may be used in other situations such as, for example, the representation of genetic dependence among organisms, within the context of statistical modeling, a MRF may be defined for any set of random variables defined at a discrete point.

TL;DR: Spatial point processes play a fundamental role in spatial statistics and are an active area of research, which probably will be of increasing importance for many new applications.