**Cheng Cheng, Ph.D.**
**St. Jude Childrenís Research
Hospital**
**Memphis,
TN**

**Abstract**

Many contemporary statistical applications, such as data mining, analysis of

microarray gene expression data, etc., require performing thousands or tens of

thousands of hypothesis testing in a single data analysis project. It seems to

be a consensus now that the control of false discovery rate (FDR) approach is

much more preferred than the control of the family-wide type-I error rate in

such applications. It is natural and practical to reject all the hull hypotheses

with corresponding p-values less than a certain significance threshold; and the

key is the determine such a threshold given the p values. Benjamini and Hochberg

developed a simple procedure to generate the threshold by controlling the FDR

at a pre-specified level. Storey considered the problem from estimation point of

view by providing an estimator of the FDR at any pre-specified significance

threshold. In practice however, it could be difficult to strike a meaningful

balance between the significance threshold and the FDR level, thus further

statistical guidance may be desirable. This research develops a practical

significance threshold determination criterion, the profile information criterion

(PIC) to complement the existing FDR methods. Genovese and Wasserman considered

the total misclassification risk and an FDR-penalized minimization of the false

non-discovery rate (FNR). In contrast to these criteria, the driving term of PIC

is a functional of the p-value uniform quantile process reflecting the ìstochastic

smallnessî of the p-values than U(0,1), instead of the FNR, and the FDR penalty is

applied in a different way. A simulation study of PIC, along with some theoretical

rationale connecting PIC to asymptotic minimax estimation, will be presented.