Regression Graph Models for Categorical Data
Parameterization and Inference
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Beschreibung
This book consolidates knowledge on regression chain graph models, often referred to as regression graph models, with a particular emphasis on their parameterizations and inference for the analysis of categorical data. It presents regression graphs, their interpretation in terms of sequences of multivariate regressions, interpretable parameterizations for categorical data, and inference and model selection within the frequentist and Bayesian approaches. The aim is to reveal the benefits of this family of graphical models for statistical data analysis and to encourage applications of these models as well as further research in the field. Data and R code used in the book are available online. The text is primarily intended for graduate and PhD students in statistics and data science who are familiar with the basics of graphical Markov models and of categorical data analysis, and for motivated researchers in specific applied fields.
This book consolidates knowledge on regression chain graph models, often referred to as regression graph models, with a particular emphasis on their parameterizations and inference for the analysis of categorical data. It presents regression graphs, their interpretation in terms of sequences of multivariate regressions, interpretable parameterizations for categorical data, and inference and model selection within the frequentist and Bayesian approaches. The aim is to reveal the benefits of this family of graphical models for statistical data analysis and to encourage applications of these models as well as further research in the field. Data and R code used in the book are available online. The text is primarily intended for graduate and PhD students in statistics and data science who are familiar with the basics of graphical Markov models and of categorical data analysis, and for motivated researchers in specific applied fields.
Consolidates knowledge on regression graph models, so far scattered in the literature Puts emphasis on parameterization and inference for categorical data analysis Reveals benefits for statistical data analysis, encouraging applications and further research in the field
Autor*in
Monia Lupparelli
Themen in »Regression Graph Models for Categorical Data«
Graphical Models Regression Graph Models Categorical Data Analysis Bi-directed Graph Models Parametrization Maximum Likelihood Inference Multivariate Logistic Regression Models Log-linear Parametrization Marginal Independence Graphical Markov Model Bayesian Learning