Majumdar, Anandamayee Paul, Debashis Zero Expectile Processes and Bayesian Spatial Regression <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> Bayesian modeling;Double normal process;Expectile;Markov chain Monte Carlo;Posterior inference;Spatial statistics 2015-06-26
    https://tandf.figshare.com/articles/dataset/Zero_Expectile_Processes_and_Bayesian_Spatial_Regression/1486460
10.6084/m9.figshare.1486460.v1