Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Regression Graph Models for Categorical Data: Parameterization and Inference

Regression Graph Models for Categorical Data: Parameterization and Inference

Lupparelli, Monia
Marchetti, Giovanni Maria
Tarantola, Claudia

Paperback

Series: Springerbriefs in Statistics

General ComputersProbability & Statistics

ISBN10: 3031997964
ISBN13: 9783031997969
Publisher: Springer
Published: Nov 14 2025
Pages: 109
Weight: 0.48
Height: 0.29 Width: 6.31 Depth: 9.24
Language: English
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.

Also from

Lupparelli, Monia

View All

Also in

Probability & Statistics

View All