The Effect of Sample Composition on Inference for Random Effects Using Normal and Dirichlet Process Models

10.6339/JDS.2010.08(4).640

Guo-Fen Yan；J. Sedransk

Bayesian nonparametric method ； extrema ； heterogeneity ； outlying clusters ； robustness

Journal of Data Science

8卷4期（2010 / 10 / 01）

579 - 595

英文

Good inference for the random effects in a linear mixed-effects model is important because of their role in decision making. For example, estimates of the random effects may be used to make decisions about the quality of medical providers such as hospitals, surgeons, etc. Standard methods assume that the random effects are normally distributed, but this may be problematic because inferences are sensitive to this assumption and to the composition of the study sample. We investigate whether using a Dirichlet process prior instead of a normal prior for the random effects is effective in reducing the dependence of inferences on the study sample. Specifically, we compare the two models, normal and Dirichlet process, emphasizing inferences for extrema. Our main finding is that using the Dirichlet process prior provides inferences that are substantially more robust to the composition of the study sample.