Smoothing spline choice in distributed lag nonlinear models for statistical modeling of count data
2
Issued Date
2024-08-27
Resource Type
ISSN
0094243X
eISSN
15517616
Scopus ID
2-s2.0-85203128333
Journal Title
AIP Conference Proceedings
Volume
3123
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
AIP Conference Proceedings Vol.3123 No.1 (2024)
Suggested Citation
Nguyen M.T.N., Nguyen V.A., Nguyen M.V.M. Smoothing spline choice in distributed lag nonlinear models for statistical modeling of count data. AIP Conference Proceedings Vol.3123 No.1 (2024). doi:10.1063/5.0224044 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101207
Title
Smoothing spline choice in distributed lag nonlinear models for statistical modeling of count data
Author(s)
Corresponding Author(s)
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Abstract
The distributed lag nonlinear model (DLNM) effectively describes the nonlinear and delayed effects in time-series investigations about some environmental exposures and health outcomes. In DLNM, nonparametric smooth functions are employed to fit the delayed nonlinear relationships between the continuous predictors and the count-dependent variable. This study focused on the cubic B-splines and cubic polynomials as reparameterization tools for these smooth functions. Furthermore, for each scenario, we apply two frameworks of the DLNM, including the classical DLNM and the penalized DLNM. A simulation study is undertaken to evaluate how well these proposed models perform, using criteria such as mean squared errors, mean absolute errors, and AIC. The penalized DLNM with a B-spline basis achieves the best performance in predicting the outcome.