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.
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Authors would like to thank the associate editor and two referees, whose comments have been very helpful in improving the manuscript.
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Mosammam, A.M., Mateu, J. A penalized likelihood method for nonseparable space–time generalized additive models. AStA Adv Stat Anal 102, 333–357 (2018). https://doi.org/10.1007/s10182-017-0309-0
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DOI: https://doi.org/10.1007/s10182-017-0309-0