Designing to discriminate between two random effect models

S. Pozuelo Campos, C. Tommasi, J. López Fidalgo, W. K. Wong

Random effect models are widely used in the literature, and considering that the model assumption is very strong in the search for the optimal design of experiments, it is interesting to discriminate between two possible models of this type. In this contribution we present theoretical results related to KL optimization and random effects models, trying to facilitate practitioners in optimal model selection through experimentation. In addition, some practical applications of the obtained results are presented.

Keywords: KL-optimality, fractional polynomials, random effect models

Scheduled

GT06.DEX2 Invited Session
November 8, 2023  4:00 PM
CC2: Conference Room

Other papers in the same session

Revisiting correlation of estimators in two parameter models: criticisms and new perspectives for optimization

J. López Fidalgo, E. R. Benítez Sastoque

Addressing the Identifiability of Parameters in the Michaelis-Menten Model through Bayesian Optimal Design

E. Benitez, J. López Fidalgo

Diseño óptimo en el caso de ingestas múltiples de alcohol

J. G. Sanchez León, J. M. Rodríguez Díaz, M. T. Santos Martín, I. Mariñas del Collado

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