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Generalized Linear Mixed Models

In: Generalized Linear Models and Extensions

Author

Listed:
  • M. Ataharul Islam

    (University of Dhaka, ISRT)

  • Soma Chowdhury Biswas

    (University of Chittagong, Department of Statistics)

Abstract

The generalized linear mixed model has emerged as a routinely employed class of linear models where both fixed and random componentsRandom component are considered for analyzing follow-up data. In a mixed model, the underlying conditional distributions for given random effects need not be Gaussian. The quasi-likelihood-based linearization, penalized quasi-likelihoodQuasi likelihood, and pseudo-likelihood-based approach are included in this chapter. This chapter provides generalized linear mixed models in a coherent manner with theoretical perspectives addressed with limitations and advantages for modeling binary, count and time-to-eventTime to event data.

Suggested Citation

  • M. Ataharul Islam & Soma Chowdhury Biswas, 2025. "Generalized Linear Mixed Models," Springer Books, in: Generalized Linear Models and Extensions, chapter 0, pages 121-137, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-4726-2_7
    DOI: 10.1007/978-981-96-4726-2_7
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