Continuous Time Modeling in the Behavioral and Related Sciences

Resource Information

The work Continuous Time Modeling in the Behavioral and Related Sciences represents a distinct intellectual or artistic creation found in European University Institute. This resource is a combination of several types including: Work, Language Material, Books.

Label

Continuous Time Modeling in the Behavioral and Related Sciences

Statement of responsibility

edited by Kees van Montfort, Johan H.L. Oud, Manuel C. Voelkle

Contributor

• Voelkle, Manuel C
• van Montfort, Kees
• Oud, Johan H.L

Editor

• van Montfort, Kees
• Oud, Johan H.L
• Voelkle, Manuel C

Subject

• Social sciences -- Data processing
• Animal behavior
• Statistical methods
• Statistics

Language

eng

Summary

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.--

Assigning source

Provided by publisher

Image bit depth

0

Literary form

non fiction

Nature of contents

dictionaries

Series statement

• Springer eBooks
• Springer eBooks.