Interval estimation in a finite mixture model: Modeling P-values in multiple testing applications
The performance of interval estimates in a uniform-beta mixture model is evaluated using three computational strategies. Such a model has found use when modeling a distribution of P-values from multiple testing applications. The number of P-values and the closeness of a parameter to the boundary of its space both play a role in the precision of parameter estimates as does the "nearness" of the beta-distribution component to the uniform distribution. Three computational strategies are compared for computing interval estimates with each one having advantages and disadvantages for cases considered here. Published by Elsevier B.V.
interval estimation bootstrap gene expression hessian mcmc microarray mle uniform beta mixture