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Analysis of Survival Data with Dependent Censoring: Copula-Based Approaches 2018 ed. [Mīkstie vāki]

  • Formāts: Paperback / softback, 84 pages, height x width: 235x155 mm, weight: 454 g, 10 Illustrations, black and white; XIII, 84 p. 10 illus., 1 Paperback / softback
  • Sērija : JSS Research Series in Statistics
  • Izdošanas datums: 13-Apr-2018
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811071632
  • ISBN-13: 9789811071638
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  • Mīkstie vāki
  • Cena: 55,83 €*
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  • Standarta cena: 65,69 €
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  • Formāts: Paperback / softback, 84 pages, height x width: 235x155 mm, weight: 454 g, 10 Illustrations, black and white; XIII, 84 p. 10 illus., 1 Paperback / softback
  • Sērija : JSS Research Series in Statistics
  • Izdošanas datums: 13-Apr-2018
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811071632
  • ISBN-13: 9789811071638
Citas grāmatas par šo tēmu:
This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring.





The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role.







The book can be used as a textbook for graduate coursework or a short course aimed at (bio-) statisticians. To deepen readers understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models.
Chapter 1: Setting the scene.
Chapter 2: Introduction to survival
analysis.
Chapter 3:  Copula models for dependent censoring.
Chapter 4:
Gene selection under dependent censoring.
Chapter 5: The joint
frailty-copula model for meta-analysis.
Chapter 6:High-dimensional
covariates in the joint frailty-copula model.
Chapter 7:Dynamic prediction
of time-to-death. Chapter 8: Future developments.- Appendix.
Takeshi Emura, Chang Gung University





 Yi-Hau Chen, Institute of Statistical Science, Academia Sinica