Simultaneous Inference for Multiple Testing and Clustering via Dirichlet Process Mixture Models David Dahl Statistics Department Texas A&M University Abstract
We propose a Bayesian
nonparametric model which exploits clustering for increased sensitivity
in multiple hypothesis testing. We build on Dahl and Newton (2007) who
showed that this was feasible by modeling the dependence among objects
through clustering and then estimating hypothesis-testing parameters
averaged over clustering uncertainty. We propose several improvements.
First, we separate the clustering of the regression coefficients from
the accommodation of heteroscedasticity. Second, our model accommodates
more general experimental designs, such as permitting covariates and
not requiring independent sampling. Third, we provide a more
satisfactory treatment of nuisance parameters and hyperparameters.
Finally, we do not require the designation of a reference treatment.
The proposed method is compared in a simulation study to ANOVA and the
BEMMA method of Dahl and Newton (2007).
This is joint work with Marina Vannucci, Michael Newton, Qianxing Mo |