Misspecifying the Shape of a Random Effects Distribution : Why Getting It Wrong May Not Matter

@inproceedings{Mcculloch2011MisspecifyingTS,
  title={Misspecifying the Shape of a Random Effects Distribution : Why Getting It Wrong May Not Matter},
  author={Charles E Mcculloch and John M Neuhaus},
  year={2011}
}
Statistical models that include random effects are commonly used to analyze longitudinal and correlated data, often with strong and parametric assumptions about the random effects distribution. There is marked disagreement in the literature as to whether such parametric assumptions are important or innocuous. In the context of generalized linear mixed models used to analyze clustered or longitudinal data, we examine the impact of random effects distribution misspecification on a variety of… CONTINUE READING

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