Zero Expectile Processes and Bayesian Spatial Regression. Issue 3 (2nd July 2016)

• Record Type: Journal Article
• Authors: Majumdar, Anandamayee; Paul, Debashis
• Is Part Of: Journal of computational and graphical statistics. Volume 25:Issue 3(2016)
• Page Start: 727
• Page End: 747
• Publication Date: 2016-07-02
• Subjects: Bayesian modeling -- Double normal process -- Expectile -- Markov chain Monte Carlo -- Posterior inference -- Spatial statistics; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2015.1062014
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract : 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.