Handbook of Multiple Comparisons (eBook, PDF)

Redaktion: Cui, Xinping; Hsu, Jason C.; Ding, Ying; Dickhaus, Thorsten

• Format: PDF

Produktbeschreibung

Treats the topics of multiple comparisons, simultaneous and selective inference from avariety of different perspectives. The need for a systematic treatment of the eld originates from the relevanceof multiple comparisons in many applications (medicine, industry, economics), and from the diversityof approaches and developments.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 418
• Erscheinungstermin: 17. November 2021
• Englisch
• ISBN-13: 9780429633881
• Artikelnr.: 62834930

Autorenporträt

Xinping Cui is professor and chair of the Department of Statistics at the University of California, Riverside, USA. Her interdisciplinary research focuses on multiple testing, statistical genomics, precision medicine and system biology. Thorsten Dickhaus is full professor of Mathematical Statistics at the University of Bremen, Germany. He is a (co-) author of approx. 50 journal articles and four books. For more than 15 years, his research focuses on simultaneous statistical inference and multiple testing. Ying Ding is Associate Professor in the Department of Biostatistics at the University of Pittsburgh. Her research focuses on survival analysis, large-scale genomics and proteomics analysis, multiple testing, and precision medicine. Jason C. Hsu is an Emeritus Professor in Statistics at the Ohio State University. His research interests are in multiple comparison, logic-respecting estimands, and targeted therapies for personalized/precision medicine.

Inhaltsangabe

1. An Overview of Multiple Comparisons. 2. Multiple Test Procedures Based
on p-Values. 3. Multivariate multiple test procedures. 4. Partitioning for
Confidence Sets, Confident Directions, and Decision Paths. 5. Graphical
approaches for multiple comparison procedures. 6. Decision Theoretic
Considerations of Multiple Comparisons. 7 Identifying important predictors
in large data bases - multiple testing and model selection. 8. Prevalence
Estimation. 9. On agnostic post hoc approaches to false positive control.
10. Group sequential and adaptive designs. 11. Multiple testing for dose
finding. 12. Multiple Endpoints. 13. Subgroups Analysis for Personalized
and Precision Medicine Development. 14. Exploratory inference: localizing
relevant effects with confidence. 15. Testing SNPs in Targeted Drug
Development.