Section on the special year for mathematics of planet earth (MPE 2013)

Abstract

Dozens of research centers, foundations, international organizations and scientific societies, including the Institute of Mathematical Statistics, have joined forces to celebrate 2013 as a special year for the Mathematics of Planet Earth. In its five-year history, the Annals of Applied Statistics has been publishing cutting edge research in this area, including geophysical, biological and socio-economic aspects of planet Earth, with the special section on statistics in the atmospheric sciences edited by Fuentes, Guttorp and Stein (2008) and the discussion paper by McShane and Wyner (2011) on paleoclimate reconstructions [Stein (2011)] having been highlights.

Published in at http://dx.doi.org/10.1214/12-AOAS606 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (15)

• Statistical Modeling of Spatial Extremes
• A hierarchical max-stable spatial model for extreme precipitation
• A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)
• Nonstationary covariance models for global data
• Dynamical functional prediction and classification, with application to traffic flow prediction
• Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping
• A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?
• Spatial analysis of wave direction data using wrapped Gaussian processes
• Phenotypic evolution studied by layered stochastic differential equations
• Approximating the conditional density given large observed values via a multivariate extremes framework, with application to environmental data
• Inference for population dynamics in the Neolithic period
• Finding a consensus on credible features among several paleoclimate reconstructions
• Gap bootstrap methods for massive data sets with an application to transportation engineering
• Editorial
• Special section on statistics in the atmospheric sciences