QA313 : Gaussian univariate and multivariate spatio-temporal models for large geostatistical data
Thesis > Central Library of Shahrood University > Mathematical Sciences > MSc > 2015
Authors:
Abstarct: Analysis of large geostatistical data sets, usually, entail the expensive matrix computations. This problem creates challenges in implementing statistical inferences of traditional Bayesian models. In addition, researchers often face with multiple spatial data sets with complex spatial dependence structures that their analysis is difficult. Complex structures of (spatial or spatio-temporal) data, their sizes and available incomplete models, have been pushed users to develope new and dynamic models. In this thesis, we use low-rank models, particularly predictive process models, to analyze Gaussian geostatistical data. This models improve MCMC sampler convergence rate and decrease sampler run-time by reducing parameter space. This improvement is due to the avoidance of expensive matrix computations that also can be used for large data sets. To evaluate the performance of this class of models, we conduct a simulation study as well as analysis of a real data set regarding the quality of underground mineral water of a large area in Golestan province, Iran.
Keywords:
#Inference Bayesian; MCMC Sampling; Predictive process; Spatio-Temporal; Geostatistical Link
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
Visitor: