A new conditional non-parametric bootstrap for NLMEMs yields better coverage for variance parameters than asymptotic methods or classical non-parametric bootstraps in sigmoid Emax model simulations across rich, sparse, and unbalanced designs.
Case bootstrap (Case):This method consists of resampling with replacement the entire subjects ( ξi,yi where yi = (yi1, yi2, ...., yini)′) from the original data before modelling
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Conditional bootstrap for non-linear mixed effects models
A new conditional non-parametric bootstrap for NLMEMs yields better coverage for variance parameters than asymptotic methods or classical non-parametric bootstraps in sigmoid Emax model simulations across rich, sparse, and unbalanced designs.