References
Dean CD, Ugarte MD, Militino AF (2001) Detecting interaction between random regions and fixed age effects in disease mapping. Biometrics 57:197–202
Goicoa T, Adin A, Ugarte MD, Hodges JS (2018) In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results. Stoch Environ Res Risk Assess 32:749–770
Goicoa T, Adin A, Etxeberria J, Militino AF, Ugarte MD (2019) Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns. Stat Methods Med Res 28:384–403
Knorr-Held L (2000) Bayesian modelling of inseparable space-time variation in disease risk. Stat Med 19:2555–2567
Leroux BG, Lei X, Breslow N (1999) Estimation of disease rates in small areas: a new mixed model for spatial dependence. In: Halloran M, Berry D (eds) Statistical models in epidemiology, the environment and clinical trials. Springer, New York, pp 179–191
Riebler A, Sørbye SH, Simpson D, Rue H (2016) An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Stat Methods Med Res 25:1145–1165
Rue H, Martino S, Chopin N (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B (Stat Methodol) 71:319–392
Ugarte MD, Adin A, Goicoa T (2017) One-dimensional, two-dimensional, and three dimensional B-splines to specify space-time interactions in Bayesian disease mapping: model fitting and model identifiability. Spat Stat 22(part 2):451–468
Acknowledgements
This work has been supported by Project MTM2017-82553-R (AEI, FEDER/UE). I would like to thank the Editor for the invitation to discuss this stimulating paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This comment refers to the invited paper available at: https://doi.org/10.1007/s11749-019-00631-z
Rights and permissions
About this article
Cite this article
Goicoa, T. Comments on: Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions. TEST 28, 40–42 (2019). https://doi.org/10.1007/s11749-019-00633-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11749-019-00633-x