Lectures on Advanced Topics in Categorical Data Analysis
Series: Springer Texts in Statistics;
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Product details:
- Edition number 2024
- Publisher Springer
- Date of Publication 17 December 2024
- Number of Volumes 1 pieces, Book
- ISBN 9783031558542
- Binding Hardback
- No. of pages377 pages
- Size 235x155 mm
- Language English
- Illustrations 25 Illustrations, black & white; 4 Illustrations, color 756
Categories
Short description:
This book continues the mission of the previous text by the author, Lectures on Categorical Data Analysis, by expanding on the introductory concepts from that volume and providing a mathematically rigorous presentation of advanced topics and current research in statistical techniques which can be applied in the social, political, behavioral, and life sciences. It presents an intuitive and unified discussion of an array of themes in categorical data analysis, and the emphasis on structure over stochastics renders many of the methods applicable in machine learning environments and for the analysis of big data.
The book focuses on graphical models, their application in causal analysis, the analytical properties of parameterizations of multivariate discrete distributions, marginal models, and coordinate-free relational models. To guide the readers in future research, the volume provides references to original papers and also offers detailed proofs of most of the significant results. Like the previous volume, it features exercises and research questions, making it appropriate for graduate students, as well as for active researchers.
MoreLong description:
This book continues the mission of the previous text by the author, Lectures on Categorical Data Analysis, by expanding on the introductory concepts from that volume and providing a mathematically rigorous presentation of advanced topics and current research in statistical techniques which can be applied in the social, political, behavioral, and life sciences. It presents an intuitive and unified discussion of an array of themes in categorical data analysis, and the emphasis on structure over stochastics renders many of the methods applicable in machine learning environments and for the analysis of big data.
The book focuses on graphical models, their application in causal analysis, the analytical properties of parameterizations of multivariate discrete distributions, marginal models, and coordinate-free relational models. To guide the readers in future research, the volume provides references to original papers and also offers detailed proofs of most of the significant results. Like the previous volume, it features exercises and research questions, making it appropriate for graduate students, as well as for active researchers.
More
Table of Contents:
1. Introduction.- 2. Undirected graphical models.- 3. Directed graphical models.- 4. Marginal models: definition.- 5. Marginal log-linear models: applications.- ?6. Path models.- 7. Relational models: definition and interpretation.- 8. Relational models as exponential families.- 9. Relational models: estimation and testing.- 10. Model testing.- 11. The mixture index of fit.
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