Error statistical modeling and inference: Where methodology meets ontology.
In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and s...
| Publicado en: | Synthese Vol. 192; no. 11; pp. 3533 - 3556 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Nov2015
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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=111504355&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 111504355 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Nov2015 vid: 192 iid: 11 pid: 237 pub: Springer Nature artinfo: ui: 111504355 10.1007/s11229-015-0744-y ppf: 3533 ppct: 23 formats: fmt: @attributes: type: P size: 487KB tig: atl: Error statistical modeling and inference: Where methodology meets ontology. aug: au: Spanos, Aris Mayo, Deborah affil: Department of Economics, Virginia Tech, Blacksburg 24061 USA Department of Philosophy, Virginia Tech, Blacksburg 24061 USA su: Statistical errors Inferential statistics Philosophy methodology Ontology Statistics Philosophy of mathematics sug: subj: Statistical errors Inferential statistics Philosophy methodology Ontology Statistics Philosophy of mathematics keyword: Error statistics Misspecification testing Replicability of inference Statistical adequacy Statistical ontology Statistical vs. substantive models ab: In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key to untangling them is the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2015. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2015 holdings: @attributes: islocal: N |
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