Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models

We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear mod...
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Autor*in:	Aliaksandr Hubin [verfasserIn] Georg Heinze [verfasserIn] Riccardo De Bin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bayesian model selection MCMC nonlinear effects

Übergeordnetes Werk:	In: Fractal and Fractional - MDPI AG, 2018, 7(2023), 641, p 641
Übergeordnetes Werk:	volume:7 ; year:2023 ; number:641, p 641

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/fractalfract7090641

Katalog-ID:	DOAJ093401779

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520			\|a We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility.
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spelling	10.3390/fractalfract7090641 doi (DE-627)DOAJ093401779 (DE-599)DOAJ398053d8dea242bd898944fbbab3bea7 DE-627 ger DE-627 rakwb eng QC310.15-319 QA1-939 QA299.6-433 Aliaksandr Hubin verfasserin aut Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility. Bayesian model selection MCMC nonlinear effects Thermodynamics Mathematics Analysis Georg Heinze verfasserin aut Riccardo De Bin verfasserin aut In Fractal and Fractional MDPI AG, 2018 7(2023), 641, p 641 (DE-627)897435656 (DE-600)2905371-7 25043110 nnns volume:7 year:2023 number:641, p 641 https://doi.org/10.3390/fractalfract7090641 kostenfrei https://doaj.org/article/398053d8dea242bd898944fbbab3bea7 kostenfrei https://www.mdpi.com/2504-3110/7/9/641 kostenfrei https://doaj.org/toc/2504-3110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 641, p 641
allfields_unstemmed	10.3390/fractalfract7090641 doi (DE-627)DOAJ093401779 (DE-599)DOAJ398053d8dea242bd898944fbbab3bea7 DE-627 ger DE-627 rakwb eng QC310.15-319 QA1-939 QA299.6-433 Aliaksandr Hubin verfasserin aut Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility. Bayesian model selection MCMC nonlinear effects Thermodynamics Mathematics Analysis Georg Heinze verfasserin aut Riccardo De Bin verfasserin aut In Fractal and Fractional MDPI AG, 2018 7(2023), 641, p 641 (DE-627)897435656 (DE-600)2905371-7 25043110 nnns volume:7 year:2023 number:641, p 641 https://doi.org/10.3390/fractalfract7090641 kostenfrei https://doaj.org/article/398053d8dea242bd898944fbbab3bea7 kostenfrei https://www.mdpi.com/2504-3110/7/9/641 kostenfrei https://doaj.org/toc/2504-3110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 641, p 641
allfieldsGer	10.3390/fractalfract7090641 doi (DE-627)DOAJ093401779 (DE-599)DOAJ398053d8dea242bd898944fbbab3bea7 DE-627 ger DE-627 rakwb eng QC310.15-319 QA1-939 QA299.6-433 Aliaksandr Hubin verfasserin aut Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility. Bayesian model selection MCMC nonlinear effects Thermodynamics Mathematics Analysis Georg Heinze verfasserin aut Riccardo De Bin verfasserin aut In Fractal and Fractional MDPI AG, 2018 7(2023), 641, p 641 (DE-627)897435656 (DE-600)2905371-7 25043110 nnns volume:7 year:2023 number:641, p 641 https://doi.org/10.3390/fractalfract7090641 kostenfrei https://doaj.org/article/398053d8dea242bd898944fbbab3bea7 kostenfrei https://www.mdpi.com/2504-3110/7/9/641 kostenfrei https://doaj.org/toc/2504-3110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 641, p 641
allfieldsSound	10.3390/fractalfract7090641 doi (DE-627)DOAJ093401779 (DE-599)DOAJ398053d8dea242bd898944fbbab3bea7 DE-627 ger DE-627 rakwb eng QC310.15-319 QA1-939 QA299.6-433 Aliaksandr Hubin verfasserin aut Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility. Bayesian model selection MCMC nonlinear effects Thermodynamics Mathematics Analysis Georg Heinze verfasserin aut Riccardo De Bin verfasserin aut In Fractal and Fractional MDPI AG, 2018 7(2023), 641, p 641 (DE-627)897435656 (DE-600)2905371-7 25043110 nnns volume:7 year:2023 number:641, p 641 https://doi.org/10.3390/fractalfract7090641 kostenfrei https://doaj.org/article/398053d8dea242bd898944fbbab3bea7 kostenfrei https://www.mdpi.com/2504-3110/7/9/641 kostenfrei https://doaj.org/toc/2504-3110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 641, p 641
language	English
source	In Fractal and Fractional 7(2023), 641, p 641 volume:7 year:2023 number:641, p 641
sourceStr	In Fractal and Fractional 7(2023), 641, p 641 volume:7 year:2023 number:641, p 641
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bayesian model selection MCMC nonlinear effects Thermodynamics Mathematics Analysis
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container_title	Fractal and Fractional
authorswithroles_txt_mv	Aliaksandr Hubin @@aut@@ Georg Heinze @@aut@@ Riccardo De Bin @@aut@@
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callnumber-first	Q - Science
author	Aliaksandr Hubin
spellingShingle	Aliaksandr Hubin misc QC310.15-319 misc QA1-939 misc QA299.6-433 misc Bayesian model selection misc MCMC misc nonlinear effects misc Thermodynamics misc Mathematics misc Analysis Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models
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topic_title	QC310.15-319 QA1-939 QA299.6-433 Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models Bayesian model selection MCMC nonlinear effects
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title	Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models
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title_sort	fractional polynomial models as special cases of bayesian generalized nonlinear models
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title_auth	Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models
abstract	We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility.
abstractGer	We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility.
abstract_unstemmed	We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility.
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title_short	Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models
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Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models

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