Graphical Markov Models: Overview
Book chapter, 2015
We describe how graphical Markov models emerged in the past 40. years, based on three essential concepts that had been developed independently more than a century ago. Sequences of joint or single regressions and their regression graphs are singled out as being the subclass that is best suited for analyzing longitudinal data and for tracing developmental pathways, both in observational and in intervention studies. Interpretations are illustrated using two sets of data. Furthermore, some of the more recent, important results for sequences of regressions are summarized.
Independence-predicting graphs
Observational studies
Regression graphs
Direct confounding
Dependence-inducing distributions
Intersection property
Issues of causality
Longitudinal studies
Intervention studies
Conditional independence
Composition property
Markov equivalence
Independence-preserving graphs
Separation criteria
Indirect confounding
Author
Nanny Wermuth
Johannes Gutenberg University Mainz
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
D. R. Cox
University of Oxford
International Encyclopedia of the Social & Behavioral Sciences: Second Edition
341-350
978-0-08-097087-5 (ISBN)
Subject Categories (SSIF 2011)
Media Engineering
Bioinformatics and Systems Biology
Probability Theory and Statistics
DOI
10.1016/B978-0-08-097086-8.42048-9