The State-Space Approach to Modeling Dynamic Processes

Moon Ho Ringo Ho*, Robert Shumway, Hernando Ombao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

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 statespace 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.

Original languageEnglish (US)
Title of host publicationModels for Intensive Longitudinal Data
PublisherOxford University Press
ISBN (Electronic)9780199847051
ISBN (Print)9780195173444
DOIs
StatePublished - Mar 22 2012

Keywords

  • Differences
  • Dynamic processes
  • Longitudinal data
  • Modeling
  • Serial correlations
  • Structures
  • Trend

ASJC Scopus subject areas

  • Psychology(all)

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