spBayesfor Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models
Year: 2015
Type: article
Abstract: In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on improving computational efficiency, flexibility, and usability for point-referenced data models. Attention is given to algorithm and computing developments that result in improved sampler convergence rate and efficiency by reducing parameter space; decreased sampler run-time by avoiding expensive matrix computations, and; increased scalability to large datasets by implementing a class of predictive process models that attempt to overcome computational hurdles by representing spatial processes in terms of lower-dimensional realizations. Beyond these general computational improvements for existing model functions, we detail new functions for modeling data indexed in both space and time. These new functions implement a class of dynamic spatio-temporal models for settings where space is viewed as continuous and time is taken as discrete.
Source: Journal of Statistical Software
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
Awards: G3087051044, Collaborative Research: Climate Change Impacts on Forest Biodiversity: Individual Risk to Subcontinental Impacts, CAREER: Advancements in Spatio-temporal Modeling and Education in Support of NEON and Large-scale and Long-term Ecological Research, Collaborative Research and NEON: MSB Category 2: PalEON - a PaleoEcological Observatory Network to Assess Terrestrial Ecosystem Models, G6070078054, Hierarchical models for Large Geostatistical Datasets with Application, G7006908063
OpenAlex ID: W1695074111