Continuous Time Modeling in the Behavioral and Related Sciences

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Megjelenés: Cham : : Springer International Publishing : : Imprint: Springer,, 2018
Kiadás:1st ed. 2018.
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Online elérés:https://doi.org/10.1007/978-3-319-77219-6
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id opac-EUL01-000979224
collection e-book
institution L_200
EUL01
spelling Continuous Time Modeling in the Behavioral and Related Sciences edited by Kees van Montfort, Johan H.L. Oud, Manuel C. Voelkle.
1st ed. 2018.
Cham : Springer International Publishing : Imprint: Springer, 2018
XI, 442 p. 95 illus., 44 illus. in color. online forrás
szöveg txt rdacontent
számítógépes c rdamedia
távoli hozzáférés cr rdacarrier
szövegfájl PDF rda
Preface -- List of contributors -- First- and Higher-Order Continuous Time Models for Arbitrary N Using SEM -- A Continuous Time Approach to Intensive Longitudinal Data: What, Why and How? -- On Fitting a Continuous Time Stochastic Process Model in the Bayesian Framework -- Understanding the Time Course of Interventions with Continuous Time Dynamic Models -- Continuous-Time Modeling of Panel Data with Network Structure -- Uses and Limitation of Continuous-Time Models to Examine Dyadic Interactions -- Makes Religion Happy - or Makes Happiness Religious? An Analysis of a Three-Wave Panel Using and Comparing Discrete and Continuous Time Techniques -- Mediation Modeling: Differing Perspectives on Time Alter Mediation Inferences -- Stochastic Differential Equation Models with Time-Varying Parameters -- Robustness of Time Delay Embedding to Sampling Interval Misspecification -- Recursive Partitioning in Continuous Time Analysis -- Continuous versus Discrete Time Modelling in Growth and Business Cycle Theory -- Continuous Time State Space Modeling with an Application to High-Frequency Road Traffic Data -- Continuous Time Modelling Based on an Exact Discrete Time Representation -- Implementation of Multivariate Continuous-Time ARMA Models -- Langevin and Kalman Importance Sampling for Nonlinear Continuous-Discrete State Space Models.
This unique book provides an overview of continuous time modeling in the behavioral and related sciences. It argues that the use of discrete time models for processes that are in fact evolving in continuous time produces problems that make their application in practice highly questionable. One main issue is the dependence of discrete time parameter estimates on the chosen time interval, which leads to incomparability of results across different observation intervals. Continuous time modeling by means of differential equations offers a powerful approach for studying dynamic phenomena, yet the use of this approach in the behavioral and related sciences such as psychology, sociology, economics and medicine, is still rare. This is unfortunate, because in these fields often only a few discrete time (sampled) observations are available for analysis (e.g., daily, weekly, yearly, etc.). However, as emphasized by Rex Bergstrom, the pioneer of continuous-time modeling in econometrics, neither human beings nor the economy cease to exist in between observations. In 16 chapters, the book addresses a vast range of topics in continuous time modeling, from approaches that closely mimic traditional linear discrete time models to highly nonlinear state space modeling techniques. Each chapter describes the type of research questions and data that the approach is most suitable for, provides detailed statistical explanations of the models, and includes one or more applied examples. To allow readers to implement the various techniques directly, accompanying computer code is made available online. The book is intended as a reference work for students and scientists working with longitudinal data who have a Master's- or early PhD-level knowledge of statistics.
Nyomtatott kiadás: ISBN 9783319772189
Nyomtatott kiadás: ISBN 9783319772202
Nyomtatott kiadás: ISBN 9783030084011
Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők.
könyv
e-book
Statistics. EUL10000081563 Y
Animal behavior. EUL10000033020 Y
Social sciences Data processing. EUL10000981646 Y
Statistical methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Behavioral Sciences.
Computer Appl. in Social and Behavioral Sciences.
Statistics for Social Sciences, Humanities, Law.
Biostatistics.
elektronikus könyv
van Montfort, Kees. szerkesztő edt http://id.loc.gov/vocabulary/relators/edt
Oud, Johan H.L. szerkesztő edt http://id.loc.gov/vocabulary/relators/edt
Voelkle, Manuel C. szerkesztő edt http://id.loc.gov/vocabulary/relators/edt
SpringerLink (Online service) közreadó testület
Online változat https://doi.org/10.1007/978-3-319-77219-6
EUL01
language English
format Book
author2 van Montfort, Kees., szerkesztő
Oud, Johan H.L., szerkesztő
Voelkle, Manuel C., szerkesztő
author_facet van Montfort, Kees., szerkesztő
Oud, Johan H.L., szerkesztő
Voelkle, Manuel C., szerkesztő
SpringerLink (Online service), közreadó testület
author_corporate SpringerLink (Online service), közreadó testület
author_sort van Montfort, Kees.
title Continuous Time Modeling in the Behavioral and Related Sciences
spellingShingle Continuous Time Modeling in the Behavioral and Related Sciences
Statistics.
Animal behavior.
Social sciences -- Data processing.
Statistical methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Behavioral Sciences.
Computer Appl. in Social and Behavioral Sciences.
Statistics for Social Sciences, Humanities, Law.
Biostatistics.
elektronikus könyv
title_short Continuous Time Modeling in the Behavioral and Related Sciences
title_full Continuous Time Modeling in the Behavioral and Related Sciences edited by Kees van Montfort, Johan H.L. Oud, Manuel C. Voelkle.
title_fullStr Continuous Time Modeling in the Behavioral and Related Sciences edited by Kees van Montfort, Johan H.L. Oud, Manuel C. Voelkle.
title_full_unstemmed Continuous Time Modeling in the Behavioral and Related Sciences edited by Kees van Montfort, Johan H.L. Oud, Manuel C. Voelkle.
title_auth Continuous Time Modeling in the Behavioral and Related Sciences
title_sort continuous time modeling in the behavioral and related sciences
publishDate 2018
publishDateSort 2018
physical XI, 442 p. 95 illus., 44 illus. in color. : online forrás
edition 1st ed. 2018.
isbn 978-3-319-77219-6
callnumber-first Q - Science
callnumber-subject QA - Mathematics
callnumber-label QA276-280
callnumber-raw 979224
callnumber-search 979224
topic Statistics.
Animal behavior.
Social sciences -- Data processing.
Statistical methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Behavioral Sciences.
Computer Appl. in Social and Behavioral Sciences.
Statistics for Social Sciences, Humanities, Law.
Biostatistics.
elektronikus könyv
topic_facet Statistics.
Animal behavior.
Social sciences -- Data processing.
Statistical methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Behavioral Sciences.
Computer Appl. in Social and Behavioral Sciences.
Statistics for Social Sciences, Humanities, Law.
Biostatistics.
elektronikus könyv
Statistics.
Animal behavior.
Social sciences
Statistical methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Behavioral Sciences.
Computer Appl. in Social and Behavioral Sciences.
Statistics for Social Sciences, Humanities, Law.
Biostatistics.
Data processing.
url https://doi.org/10.1007/978-3-319-77219-6
illustrated Not Illustrated
dewey-hundreds 500 - Science
dewey-tens 510 - Mathematics
dewey-ones 519 - Probabilities & applied mathematics
dewey-full 519.5
dewey-sort 3519.5
dewey-raw 519.5
dewey-search 519.5
first_indexed 2023-12-26T23:12:09Z
last_indexed 2023-12-29T19:17:07Z
recordtype opac
publisher Cham : : Springer International Publishing : : Imprint: Springer,
_version_ 1786641203208388608
score 13,371199
generalnotes This unique book provides an overview of continuous time modeling in the behavioral and related sciences. It argues that the use of discrete time models for processes that are in fact evolving in continuous time produces problems that make their application in practice highly questionable. One main issue is the dependence of discrete time parameter estimates on the chosen time interval, which leads to incomparability of results across different observation intervals. Continuous time modeling by means of differential equations offers a powerful approach for studying dynamic phenomena, yet the use of this approach in the behavioral and related sciences such as psychology, sociology, economics and medicine, is still rare. This is unfortunate, because in these fields often only a few discrete time (sampled) observations are available for analysis (e.g., daily, weekly, yearly, etc.). However, as emphasized by Rex Bergstrom, the pioneer of continuous-time modeling in econometrics, neither human beings nor the economy cease to exist in between observations. In 16 chapters, the book addresses a vast range of topics in continuous time modeling, from approaches that closely mimic traditional linear discrete time models to highly nonlinear state space modeling techniques. Each chapter describes the type of research questions and data that the approach is most suitable for, provides detailed statistical explanations of the models, and includes one or more applied examples. To allow readers to implement the various techniques directly, accompanying computer code is made available online. The book is intended as a reference work for students and scientists working with longitudinal data who have a Master's- or early PhD-level knowledge of statistics.