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Adaptive 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 observ...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189237/ https://www.ncbi.nlm.nih.gov/pubmed/37362848 http://dx.doi.org/10.1007/s00477-023-02466-5 |
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author | Maranzano, Paolo Otto, Philipp Fassò, Alessandro |
author_facet | Maranzano, Paolo Otto, Philipp Fassò, Alessandro |
author_sort | Maranzano, Paolo |
collection | PubMed |
description | 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 computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00477-023-02466-5. |
format | Online Article Text |
id | pubmed-10189237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-101892372023-05-19 Adaptive LASSO estimation for functional hidden dynamic geostatistical models Maranzano, Paolo Otto, Philipp Fassò, Alessandro Stoch Environ Res Risk Assess Original Paper 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 computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00477-023-02466-5. Springer Berlin Heidelberg 2023-05-17 /pmc/articles/PMC10189237/ /pubmed/37362848 http://dx.doi.org/10.1007/s00477-023-02466-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Paper Maranzano, Paolo Otto, Philipp Fassò, Alessandro Adaptive LASSO estimation for functional hidden dynamic geostatistical models |
title | Adaptive LASSO estimation for functional hidden dynamic geostatistical models |
title_full | Adaptive LASSO estimation for functional hidden dynamic geostatistical models |
title_fullStr | Adaptive LASSO estimation for functional hidden dynamic geostatistical models |
title_full_unstemmed | Adaptive LASSO estimation for functional hidden dynamic geostatistical models |
title_short | Adaptive LASSO estimation for functional hidden dynamic geostatistical models |
title_sort | adaptive lasso estimation for functional hidden dynamic geostatistical models |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189237/ https://www.ncbi.nlm.nih.gov/pubmed/37362848 http://dx.doi.org/10.1007/s00477-023-02466-5 |
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