Testing hypotheses on a tree: new error rates and controlling strategies
note special time
In modern statistical challenges we are often presented with a set of families of hypotheses which are organized hierarchically in a tree structure.
Each family is selected and tested only if all its ancestor hypotheses are rejected. We formulate a general class of error rates addressing selective inference
on families of hypotheses which are organized in a tree structure, and propose a hierarchical testing procedure with a guaranteed control of such error rates.
Joint work with Yoav Benjamini, Christine Burns Peterson, and Chiara Sabatti