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Continuous Time Modeling In The Behavioral And Related Sciences 1st Ed Kees Van Montfort

  • SKU: BELL-7322140
Continuous Time Modeling In The Behavioral And Related Sciences 1st Ed Kees Van Montfort
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Continuous Time Modeling In The Behavioral And Related Sciences 1st Ed Kees Van Montfort instant download after payment.

Publisher: Springer International Publishing
File Extension: PDF
File size: 12.54 MB
Author: Kees van Montfort, Johan H.L. Oud, Manuel C. Voelkle
ISBN: 9783319772189, 9783319772196, 331977218X, 3319772198
Language: English
Year: 2018
Edition: 1st ed.

Product desciption

Continuous Time Modeling In The Behavioral And Related Sciences 1st Ed Kees Van Montfort by Kees Van Montfort, Johan H.l. Oud, Manuel C. Voelkle 9783319772189, 9783319772196, 331977218X, 3319772198 instant download after payment.

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.

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