Robust designs for generalized linear mixed models with possible model misspecification

  title={Robust designs for generalized linear mixed models with possible model misspecification},
  author={Xiaojian Xu and Sanjoy K. Sinha},
  journal={Journal of Statistical Planning and Inference},

A random model for the scale parameter in the Fréchet populations

This paper deals with one-way classification analysis when the response variable follows the one-parameter Frechet distribution and the factor effects are random. The stochastic properties of the

Robust designs for dose-response studies: Model and labelling robustness

  • D. Wiens
  • Mathematics
    Comput. Stat. Data Anal.
  • 2021

Model-robust Bayesian design through Generalised Additive Models for monitoring submerged shoals

A Bayesian design strategy to optimise sampling for a shoal deep reef system using three years of pilot data is developed and applied to design future monitoring of sub-merged shoals on the north-west coast of Australia with the aim of improving on current monitoring designs.



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A method is proposed for finding exact designs for experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search.

Bayesian D-Optimal Designs for Poisson Regression Models

By incorporating informative and/or historical knowledge of the unknown parameters, Bayesian experimental design under the decision-theory framework can combine all the information available to the

Bayesian D-optimal design for logistic regression model with exponential distribution for random intercept

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Misspecified maximum likelihood estimates and generalised linear mixed models

SUMMARY We investigate the impact of model violations on the estimate of a regression coefficient in a generalised linear mixed model. Specifically, we evaluate the asymptotic relative bias that