Zi Ye

My research generally focuses on multivariate and nonparametric analysis. I am interested in research involving novel statistical methodology development and the derivation of companion supporting theories. One of mine research projects focus on the misclassification errors that are common in the real world. To assess the efficacy and safety of a treatment, we sometimes need to classify participants into different groups based on results from diagnostic devices. For example, we need a covid test kit to determine if someone has covid. However, such diagnostic devices usually do not have perfect accuracy, and their misclassification rates are unknown. The traditional method ignores this problem and assumes the diagnostic devices are perfect. This assumption will lead to inefficient and biased estimators. In this era of personalized medicine and measurement-based care, this issue of misclassification is prominent. However, most works focus on assessing the diagnostic devices themselves. Only a few works evaluate the clinical validity of treatment in the presence of diagnostic errors. My research aims to fill in this methodological gap.