R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework

Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the ac...
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Autor*in:	Laura Deldossi [verfasserIn] Marta Nai Ruscone [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	bayesian design of experiments screening experiments bayesian model selection model discrimination

Übergeordnetes Werk:	In: Journal of Statistical Software - Foundation for Open Access Statistics, 2003, 94(2020), 1, Seite 37
Übergeordnetes Werk:	volume:94 ; year:2020 ; number:1 ; pages:37

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.18637/jss.v094.i02

Katalog-ID:	DOAJ008020078

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spelling	10.18637/jss.v094.i02 doi (DE-627)DOAJ008020078 (DE-599)DOAJbc3d0a6d13d0452a93b6f255faaa831b DE-627 ger DE-627 rakwb eng HA1-4737 Laura Deldossi verfasserin aut R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the active factors. The R package OBsMD solves this problem by implementing the objective Bayesian methodology proposed by Consonni and Deldossi (2016). The main feature of this approach is that the follow-up designs are obtained through the use of just two functions, OBsProb() and OMD() without requiring any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for conducting a design of experiments to solve real world problems. bayesian design of experiments screening experiments bayesian model selection model discrimination Statistics Marta Nai Ruscone verfasserin aut In Journal of Statistical Software Foundation for Open Access Statistics, 2003 94(2020), 1, Seite 37 (DE-627)313105669 (DE-600)2010240-9 15487660 nnns volume:94 year:2020 number:1 pages:37 https://doi.org/10.18637/jss.v094.i02 kostenfrei https://doaj.org/article/bc3d0a6d13d0452a93b6f255faaa831b kostenfrei https://www.jstatsoft.org/index.php/jss/article/view/3024 kostenfrei https://doaj.org/toc/1548-7660 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 94 2020 1 37
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callnumber-first	H - Social Science
author	Laura Deldossi
spellingShingle	Laura Deldossi misc HA1-4737 misc bayesian design of experiments misc screening experiments misc bayesian model selection misc model discrimination misc Statistics R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework
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topic_title	HA1-4737 R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework bayesian design of experiments screening experiments bayesian model selection model discrimination
topic	misc HA1-4737 misc bayesian design of experiments misc screening experiments misc bayesian model selection misc model discrimination misc Statistics
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title	R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework
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title_full	R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework
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title_sort	r package obsmd for follow-up designs in an objective bayesian framework
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title_auth	R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework
abstract	Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the active factors. The R package OBsMD solves this problem by implementing the objective Bayesian methodology proposed by Consonni and Deldossi (2016). The main feature of this approach is that the follow-up designs are obtained through the use of just two functions, OBsProb() and OMD() without requiring any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for conducting a design of experiments to solve real world problems.
abstractGer	Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the active factors. The R package OBsMD solves this problem by implementing the objective Bayesian methodology proposed by Consonni and Deldossi (2016). The main feature of this approach is that the follow-up designs are obtained through the use of just two functions, OBsProb() and OMD() without requiring any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for conducting a design of experiments to solve real world problems.
abstract_unstemmed	Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the active factors. The R package OBsMD solves this problem by implementing the objective Bayesian methodology proposed by Consonni and Deldossi (2016). The main feature of this approach is that the follow-up designs are obtained through the use of just two functions, OBsProb() and OMD() without requiring any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for conducting a design of experiments to solve real world problems.
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title_short	R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework
url	https://doi.org/10.18637/jss.v094.i02 https://doaj.org/article/bc3d0a6d13d0452a93b6f255faaa831b https://www.jstatsoft.org/index.php/jss/article/view/3024 https://doaj.org/toc/1548-7660
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