Simultaneous Statistical Inference - With Applications in the Life Sciences
Reduzierte Artikel in dieser Kategorie
| Verlag | Springer |
| Auflage | 2016 |
| Seiten | 180 |
| Format | 14,2 x 23,5 x 1,3 cm |
| Previously published in hardcover | |
| Gewicht | 318 g |
| Artikeltyp | Englisches Buch |
| ISBN-10 | 3662510065 |
| EAN | 9783662510063 |
| Bestell-Nr | 66251006A |
This monograph offers an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate and related error measures, particularly addressing applications to such fields as genetics, proteomics and neuroscience.
Klappentext:
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.
Inhaltsverzeichnis:
The problem of simultaneous inference.- Part I General Theory.- Some theory of p-values.- Classes of multiple test procedures.- Simultaneous test procedures.- Stepwise rejective multiple tests.- Multiple testing and binary classification.- Multiple testing and model selection.- Software solutions for multiple hypotheses testing.- Part II From Genotype to Phenotype.- Genetic association studies.- Gene expression analyses.- Functional magnetic resonance imaging.- Part III Further Applications in the Life Sciences.- Further life science applications.
Rezension:
"Thorsten Dickhaus' Simultaneous Statistical Inference is without a doubt the most thorough yet concise roundup of multiple-test procedures that has come out in many years. ... It is all the more worthwhile reading for statistical researchers, who will be guided through the maze of multiple-testing approaches that have accumulated over the past decades. ... a rich source of inspiration for anyone who has some mathematical background and seeks a deep understanding of state-of-the-art simultaneous inference." (Philip Pallmann, Biometrical Journal, Vol. 57 (6), 2015)