Bernoulli

  • Bernoulli
  • Volume 17, Number 3 (2011), 845-879.

Probability distributions with summary graph structure

Nanny Wermuth

Full-text: Open access

Abstract

A set of independence statements may define the independence structure of interest in a family of joint probability distributions. This structure is often captured by a graph that consists of nodes representing the random variables and of edges that couple node pairs. One important class contains regression graphs. Regression graphs are a type of so-called chain graph and describe stepwise processes, in which at each step single or joint responses are generated given the relevant explanatory variables in their past. For joint densities that result after possible marginalising or conditioning, we introduce summary graphs. These graphs reflect the independence structure implied by the generating process for the reduced set of variables and they preserve the implied independences after additional marginalising and conditioning. They can identify generating dependences that remain unchanged and alert to possibly severe distortions due to direct and indirect confounding. Operators for matrix representations of graphs are used to derive these properties of summary graphs and to translate them into special types of paths in graphs.

Article information

Source
Bernoulli, Volume 17, Number 3 (2011), 845-879.

Dates
First available in Project Euclid: 7 July 2011

Permanent link to this document
https://projecteuclid.org/euclid.bj/1310042847

Digital Object Identifier
doi:10.3150/10-BEJ309

Mathematical Reviews number (MathSciNet)
MR2817608

Zentralblatt MATH identifier
1245.62062

Keywords
concentration graph directed acyclic graph endogenous variables graphical Markov model independence graph multivariate regression chain partial closure partial inversion triangular system

Citation

Wermuth, Nanny. Probability distributions with summary graph structure. Bernoulli 17 (2011), no. 3, 845--879. doi:10.3150/10-BEJ309. https://projecteuclid.org/euclid.bj/1310042847


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