Multivariate Bonferroni-Type Inequalities
eBook - PDF

Multivariate Bonferroni-Type Inequalities

Theory and Applications

  1. 302 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Multivariate Bonferroni-Type Inequalities

Theory and Applications

About this book

Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Frechet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamilton circuits.

The first half of the book describes basic concepts and methods in probability inequalities. The author introduces the classification of univariate and multivariate bounds with optimality, discusses multivariate bounds using indicator functions, and explores linear programming for bivariate upper and lower bounds.

The second half addresses bounding results and applications of multivariate Bonferroni-type inequalities. The book shows how to construct new multiple testing procedures with probability upper bounds and goes beyond bivariate upper bounds by considering vectorized upper and hybrid bounds. It presents an optimization algorithm for bivariate and multivariate lower bounds and covers vectorized high-dimensional lower bounds with refinements, such as Hamilton-type circuits and sub-Markovian events. The book concludes with applications of probability inequalities in molecular cancer therapy, big data analysis, and more.

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Yes, you can access Multivariate Bonferroni-Type Inequalities by John Chen in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Front Cover
  2. Dedication
  3. Contents
  4. List of Figures
  5. List of Tables
  6. Preface
  7. Chapter 1 Introduction
  8. Chapter 2 Fundamentals
  9. Chapter 3 Multivariate Indicator Functions
  10. Chapter 4 Multivariate Linear Programming Framework
  11. Chapter 5 Bivariate Upper Bounds
  12. Chapter 6 Multivariate and Hybrid Upper Bounds
  13. Chapter 7 Bivariate Lower Bounds
  14. Chapter 8 Multivariate and Hybrid Lower Bounds
  15. Chapter 9 Case Studies
  16. Bibliography
  17. Back Cover