Designs for Generalized Linear Models
Anthony C. Atkinson, David C. Woods
TL;DR
This paper reviews experimental design strategies for generalized linear models, covering optimal, Bayesian, and random effects designs to improve statistical inference and model efficiency.
Contribution
It provides a comprehensive overview of design methods tailored for generalized linear models, highlighting recent advances and practical approaches.
Findings
Summarizes optimal design techniques for GLMs
Discusses Bayesian design strategies for GLMs
Addresses design considerations for models with random effects
Abstract
This paper reviews the design of experiments for generalised linear models, including optimal design, Bayesian design and designs for models with random effects.
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Taxonomy
TopicsOptimal Experimental Design Methods · Advanced Multi-Objective Optimization Algorithms