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dc.contributor.author
Bringmann, Laura F.
dc.contributor.author
Hamaker, Ellen L.
dc.contributor.author
Vigo, Daniel Eduardo
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dc.contributor.author
Aubert, André
dc.contributor.author
Borsboom, Denny
dc.contributor.author
Tuerlinckx, Francis
dc.date.available
2018-03-20T16:19:22Z
dc.date.issued
2017-09
dc.identifier.citation
Bringmann, Laura F.; Hamaker, Ellen L.; Vigo, Daniel Eduardo; Aubert, André; Borsboom, Denny; et al.; Changing dynamics: Time-varying autoregressive models using generalized additive modeling; American Psychological Association; Psychological Methods; 22; 3; 9-2017; 409-425
dc.identifier.issn
1082-989X
dc.identifier.uri
http://hdl.handle.net/11336/39349
dc.description.abstract
In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Psychological Association
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Time Series
dc.subject
Nonstationarity
dc.subject
Autoregressive Models
dc.subject
Generalized Additive Models
dc.subject
Splines
dc.subject.classification
Inmunología
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dc.subject.classification
Medicina Básica
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Changing dynamics: Time-varying autoregressive models using generalized additive modeling
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-03-20T14:33:36Z
dc.identifier.eissn
1939-1463
dc.journal.volume
22
dc.journal.number
3
dc.journal.pagination
409-425
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Bringmann, Laura F.. Katholikie Universiteit Leuven; Bélgica
dc.description.fil
Fil: Hamaker, Ellen L.. University of Utrecht; Países Bajos
dc.description.fil
Fil: Vigo, Daniel Eduardo. Pontificia Universidad Católica Argentina ; Argentina
dc.description.fil
Fil: Aubert, André. Katholikie Universiteit Leuven; Bélgica
dc.description.fil
Fil: Borsboom, Denny. University of Amsterdam; Países Bajos
dc.description.fil
Fil: Tuerlinckx, Francis. Katholikie Universiteit Leuven; Bélgica
dc.journal.title
Psychological Methods
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1037/met0000085
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://psycnet.apa.org/doiLanding?doi=10.1037%2Fmet0000085
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