Introduction to Functional Data Analysis.
Psychologists and behavioural scientists are increasingly collecting data that are drawn from continuous underlying processes. We describe a set of quantitative methods, Functional Data Analysis (FDA), which can answer a number of questions that traditional statistical approaches cannot. These metho...
| Publicado en: | Canadian Psychology Vol. 48; no. 3; pp. 135 - 156 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Canadian Psychological Association
August 2007
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=510673377&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 510673377 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07085591 G48 jtl: Canadian Psychology issn: 07085591 maglogo: N pubinfo: dt: August 2007 vid: 48 iid: 3 pid: 98 pub: Canadian Psychological Association artinfo: ui: 510673377 10.1037/cp2007014 ppf: 135 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.8MB tig: atl: Introduction to Functional Data Analysis. aug: au: Levitin, Daniel J. Nuzzo, Regina L. Vines, Bradley W. Ramsay, J. O. su: Psychological techniques Psychology -- Statistical methods Multivariate analysis sug: subj: Psychological techniques Psychology -- Statistical methods Multivariate analysis ab: Psychologists and behavioural scientists are increasingly collecting data that are drawn from continuous underlying processes. We describe a set of quantitative methods, Functional Data Analysis (FDA), which can answer a number of questions that traditional statistical approaches cannot. These methods are applicable for analyzing many datasets that are common in experimental psychology, including time series data, repeated measures, and data distributed over time or space as in neuroimaging experiments. The primary advantage of FDA is that it allows the researcher to ask questions about when in a time series differences may exist between two or more sets of observations. We discuss functional correlations, principal components, the derivatives of functional curves, and analysis of variances models. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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