Contaminated normal modeling with application to microarray data analysis

A contaminated beta model (1 - γ)B(1, 1) + γB(α, β) is often used to describe the distribution of P-values arising from a microarray experiment. The authors propose and examine a different approach: namely, using a contaminated normal model (1 - γ)N(0, σ2) + γN(μ, σ2) to describe the distribution of Z statistics or suitably transformed T statistics. The authors then address whether a researcher who has Z statistics should analyze them using the contaminated normal model or whether the Z statistics should be converted to P-values to be analyzed using the contaminated beta model. The authors also provide a decisiontheoretic perspective on the analysis of Z statistics.