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1. Regression Graph Models

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Monia Lupparelli
Giovanni Maria Marchetti
Claudia Tarantola
Published in
Regression Graph Models for Categorical Data
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces regression graph models, a class of graphical Markov models that define specific conditional independence constraints for systems of random variables. These models are useful for specifying stepwise data-generating processes and research hypotheses in cohort studies. The chapter begins with definitions and properties common to all graphical models, followed by a detailed discussion of Gaussian variables. It explores the concepts of marginal and conditional independence, the role of variables in statistical studies, and the interpretation of different types of graphs. Practical examples, such as labor market studies and lawyer's ratings of state judges, illustrate the application of these models. The chapter also covers the connected set Markov property, conditional covariance graphs, and multivariate regression graphs. It concludes with applications to categorical data and the interpretation of regression chain graph models. Readers will gain a comprehensive understanding of regression graph models and their practical applications in statistical analysis.
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next chapter Multivariate Logistic Regression Models
Title
Regression Graph Models
Authors
Monia Lupparelli
Giovanni Maria Marchetti
Claudia Tarantola
Copyright Year
2025
Book
Regression Graph Models for Categorical Data

Print ISBN: 978-3-031-99796-9

Electronic ISBN: 978-3-031-99797-6

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-99797-6_1
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