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This work aims to present and compare likelihood and Bayesian approaches for generalized linear models.

July 18, 2026Journal of the American Statistical AssociationOpen Access

Double Generalized Linear Models: Likelihood and Bayesian Methods

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This work aims to present and compare likelihood and Bayesian approaches for generalized linear models.
Comprehensive exploration of double generalized linear models.
Comparison of likelihood and Bayesian methods for effective parameter estimation.
Provides insight into statistical inference techniques in various application areas.
Demonstrates advantages of Bayesian methods over traditional likelihood approaches in specific contexts.

Implication

Book explores statistical methods for parameter estimation in generalized linear models, enabling improved analysis.

Abstract

Edilberto Cepeda-Cuervo. Boca Raton, FL: Chapman & Hall/CRC Press, 2026, xiii + 146 pp., $159.99(e-book), $66.99(H), ISBN: 978-1-003-68741-2(e-book), ISBN: 978-1-041-16997-0(H).In generalized linea...

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Cite This Study

Yang Ni (2026) studied this question.

synapsesocial.com/papers/6a5b172618557b26c20397bf https://doi.org/10.1080/01621459.2026.2704884

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Implication

Book explores statistical methods for parameter estimation in generalized linear models, enabling improved analysis.

Double Generalized Linear Models: Likelihood and Bayesian Methods

Key Points

Implication

Abstract

Discussion

Cite This Study

Double Generalized Linear Models: Likelihood and Bayesian Methods

Key Points

Implication

Abstract

Discussion

Cite This Study

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