Description
Double Generalized Linear Models (DGLMs) represent an important advancement in statistical modeling, extending traditional generalized linear models to handle more complex data structures and relationships. This comprehensive work by Edilberto Cepeda-Cuervo examines both likelihood-based and Bayesian methodologies for implementing DGLMs.
The book provides practitioners and researchers with detailed theoretical foundations and practical applications of double generalized linear models. It covers parameter estimation, inference procedures, and computational techniques essential for working with these sophisticated statistical tools. The author presents clear explanations of how DGLMs can be applied to real-world problems where conventional models fall short, particularly in cases involving heterogeneous variance or complex dependencies.
Published by Chapman & Hall, a respected publisher of statistical literature, this volume serves as both a reference text and practical guide. It bridges the gap between classical frequentist approaches and modern Bayesian methods, offering researchers flexible options for their specific modeling needs and allowing for more accurate analysis of intricate datasets.
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