10.6084/m9.figshare.1486460.v1
Anandamayee Majumdar
Anandamayee
Majumdar
Debashis Paul
Debashis
Paul
Zero Expectile Processes and Bayesian Spatial Regression
Taylor & Francis Group
2015
Bayesian modeling
Double normal process
Expectile
Markov chain Monte Carlo
Posterior inference
Spatial statistics
2015-06-26 00:00:00
Dataset
https://tandf.figshare.com/articles/dataset/Zero_Expectile_Processes_and_Bayesian_Spatial_Regression/1486460
<p>We introduce new classes of stationary spatial processes with asymmetric, sub-Gaussian marginal distributions using the idea of expectiles. We derive theoretical properties of the proposed processes. Moreover, we use the proposed spatial processes to formulate a spatial regression model for point-referenced data where the spatially correlated errors have skewed marginal distribution. We introduce a Bayesian computational procedure for model fitting and inference for this class of spatial regression models. We compare the performance of the proposed method with the traditional Gaussian process-based spatial regression through simulation studies and by applying it to a dataset on air pollution in California.</p>