Science without (parametric) models: the case of bootstrap resampling.
Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alter...
| Publicado en: | Synthese Vol. 180; no. 1; pp. 65 - 77 |
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| Formato: | Artículo |
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Springer Nature
May2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=59671729&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 59671729 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: May2011 vid: 180 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 59671729 10.1007/s11229-009-9567-z ppf: 65 ppct: 12 formats: fmt: @attributes: type: P size: 160KB tig: atl: Science without (parametric) models: the case of bootstrap resampling. aug: au: Sprenger, Jan affil: Tilburg Center for Logic and Philosophy of Science, Tilburg University, 5000 LE Tilburg The Netherlands su: Statistical hypothesis testing Experimental design Nonparametric statistics Statistical bootstrapping Philosophical literature sug: subj: Statistical hypothesis testing Experimental design Nonparametric statistics Statistical bootstrapping Philosophical literature keyword: Bootstrap resampling Data Inductive inference Models ab: Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2011. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2011 holdings: @attributes: islocal: N |
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