Graphical Markov Models: Overview
Kapitel i bok, 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

Författare

Nanny Wermuth

Johannes Gutenberg-Universität Mainz

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

D. R. Cox

University of Oxford

International Encyclopedia of the Social & Behavioral Sciences: Second Edition

341-350

Ämneskategorier

Mediateknik

Bioinformatik och systembiologi

Sannolikhetsteori och statistik

DOI

10.1016/B978-0-08-097086-8.42048-9

Mer information

Senast uppdaterat

2019-10-17