Inference with Contaminated Data: Harnessing the potentials of nonparametric finite mixtures

Music City Center 

In some studies, participants are first classified as either having or not having the characteristic of interest based on diagnostic tools, but such classifiers may not be perfectly accurate. Diagnostic misclassification has been shown to introduce severe bias in estimating treatment effects and lead to grossly inaccurate inferences. We aim to address these problems in a fully nonparametric setting. Methods for estimating and testing meaningful yet nonparametric treatment effects are developed. The proposed methods apply to outcomes measured on ordinal, discrete, or continuous scales. They do not require any assumptions, such as the existence of moments. The applications of the proposed methods are illustrated using gene expression profiling of bronchial airway brushing in asthmatic and healthy control subjects.