Sigmoidal curves, very common in epidemiology and biology, have traditionally been fitted using parametric models or fully non-parametric approaches like splines. In this paper, we propose a semi-parametric approach which is flexible enough to capture several sigmoidal shapes. The estimation procedure is iterative and relies on a first-order Taylor expansion around the inflection point. The performance of our approach is compared to some parametric models through a simulation study and an application to data. Results of simulations show that our approach performs well in terms of mean integrated squared errors in a variety of scenarios.

Chiara Di Maria, Vito Muggeo (2023). Semi-parametric estimation of growth curves. In Proceedings of the 37th International Workshop on Statistical Modelling. Elisabeth Bergherr, Andreas Groll, Andreas Mayr.

Semi-parametric estimation of growth curves

Chiara Di Maria
;
Vito Muggeo
2023-01-01

Abstract

Sigmoidal curves, very common in epidemiology and biology, have traditionally been fitted using parametric models or fully non-parametric approaches like splines. In this paper, we propose a semi-parametric approach which is flexible enough to capture several sigmoidal shapes. The estimation procedure is iterative and relies on a first-order Taylor expansion around the inflection point. The performance of our approach is compared to some parametric models through a simulation study and an application to data. Results of simulations show that our approach performs well in terms of mean integrated squared errors in a variety of scenarios.
2023
978-3-947323-42-5
Chiara Di Maria, Vito Muggeo (2023). Semi-parametric estimation of growth curves. In Proceedings of the 37th International Workshop on Statistical Modelling. Elisabeth Bergherr, Andreas Groll, Andreas Mayr.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/607804
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