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Models for Intensive Longitudinal Data$
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Theodore A. Walls and Joseph L. Schafer

Print publication date: 2006

Print ISBN-13: 9780195173444

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780195173444.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 07 December 2019

The State-Space Approach to Modeling Dynamic Processes

The State-Space Approach to Modeling Dynamic Processes

Chapter:
(p.148) 7 The State-Space Approach to Modeling Dynamic Processes
Source:
Models for Intensive Longitudinal Data
Author(s):

Moon-Ho Ringo Ho

Robert Shumway

Hernando Ombao

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195173444.003.0007

In this chapter, the authors seek to present a self-contained treatment of state-space modeling and attempt to make the exposition accessible to those who have relatively little prior knowledge of the subject. They focus on issues of modeling and show how state-space models offer a flexible and rich class of structures that accommodate both the dynamic and static nature of intensive longitudinal data. Longitudinal data obtained from a group or group of subjects followed over time often show within-subject serial correlations, involving random subject effects and the presence of observational errors. Researchers are usually interested in describing the trend over time, whether there are critical differences in the trend across groups of subjects, and what factors can be considered for this trend and the differences.

Keywords:   modeling, dynamic processes, structures, longitudinal data, serial correlations, trend, differences

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