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A penalized likelihood method for nonseparable space–time generalized additive models

Ali M. Mosammam () and Jorge Mateu
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Ali M. Mosammam: University of Zanjan
Jorge Mateu: Universitat Jaume I

AStA Advances in Statistical Analysis, 2018, vol. 102, issue 3, 333-357

Abstract: Abstract In this paper, we study space–time generalized additive models. We apply the penalyzed likelihood method to fit generalized additive models (GAMs) for nonseparable spatio-temporal correlated data in order to improve the estimation of the response and smooth terms of GAMs. The results show that our space–time generalized additive models estimated response and smooth terms reasonable well, and in addition, the mean squared error, mean absolute deviation and coverage intervals improved considerably compared to the classic GAM. An application on particulate matter concentration in the North-Italian region of Piemonte is also presented.

Keywords: Interpolation; Regression kriging; Smoothing; Spatial-temporal process (search for similar items in EconPapers)
Date: 2018
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