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Titlebook: Continuous Time Modeling in the Behavioral and Related Sciences; Kees van Montfort,Johan H. L. Oud,Manuel C. Voelkl Book 2018 Springer Int

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書(shū)目名稱Continuous Time Modeling in the Behavioral and Related Sciences
編輯Kees van Montfort,Johan H. L. Oud,Manuel C. Voelkl
視頻videohttp://file.papertrans.cn/238/237024/237024.mp4
概述This is the first and only book on continuous time modeling in the behavioral and related sciences.Introduces Newtonian dynamics modeling by means of differential equations for behavioral and related
圖書(shū)封面Titlebook: Continuous Time Modeling in the Behavioral and Related Sciences;  Kees van Montfort,Johan H. L. Oud,Manuel C. Voelkl Book 2018 Springer Int
描述.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, neitherhuman 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 tradition
出版日期Book 2018
關(guān)鍵詞37N40, 62M10, 62P15, 62P25, 65F60, 91B99, 91D30, 93E99, 97M70; continuous time modeling; recursive par
版次1
doihttps://doi.org/10.1007/978-3-319-77219-6
isbn_softcover978-3-030-08401-1
isbn_ebook978-3-319-77219-6
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Continuous Time Modelling Based on an Exact Discrete Time Representation,estimation of the exact discrete time model, alternative time domain (state space) and frequency domain approaches are also discussed. Computational issues are explored, and two new empirical applications are included along with a discussion of applications in the field of macroeconometric modelling.
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Die ungleiche Verteilung des Glücks the combination of a weighted difference between long-term expectations and daily ratings as well as daily ratings between partners in the dyad. To verify the correct specification, we used simulated models using the LSDE SAS/IML package developed by Singer.
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Peter Atteslander,Hans-Ulrich Kneubühlerails of the continuous time state space methodology and review the similarities and the differences between the continuous time and discrete time approaches. An application in the modelling of road traffic data is presented in order to illustrate the relevance of continuous time state space modelling in practice.
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Book 2018els 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
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