biostudies-literatureUnknownMaranzano PWe propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The comStochastic environmental research and risk assessment : research journal1-23https://www.ebi.ac.uk/biostudies/studies/S-EPMC10189237biostudies-literatureAdaptive LASSO estimation for functional hidden dynamic geostatistical models.PMC10189237Fasso AMaranzano POtto PfalseAdaptive LASSO estimation for functional hidden dynamic geostatistical models.We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The com2023-01-01T00:00:00Z2023 May2025-04-21T17:10:45.17Z2025-04-21T17:10:45.17ZS-EPMC101892373736284810.1007/s00477-023-02466-5