Table of contents
About this book
This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover, new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students.
Binary Classification Error Measures for High-dimensional Data False Discovery Rate Large-scale Problems in the Life Sciences Least Favorable Parameter Configurations Multiple Testing Theory Multiple Tests for Discrete Data Simultaneous Statistical Inference Step-up-down Tests