Multivariate Bonferroni-Type Inequalities

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A01=John Chen
advanced statistical optimization
Advances In Bounding Techniques
ANOVA Assumption
Applications Of Probability Inequalities
Author_John Chen
Bonferroni Inequality
Bounds Using Hamilton Circuits
Category=PBT
Elementary Conjunctions
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Event A1
Familywise Error Rate
high-dimensional data analysis
Indicator Functions
Inequality Method
Linear And Fr?Et Optimality
Linear Programming
Linear Programming Bound
Linear Programming For Multivariate Bounds
Lower Bound
Minimum Effective Dose
molecular therapy statistics
multiple hypothesis testing
Multiple Testing Procedures
multivariate probability inequalities applications
Optimal Bound
Optimal Upper Bound
Optimization Algorithm For Bivariate And Multivariate Lower Bounds
probability bounds
Probability Inequalities
Probability Space
Residual Thrombus
Simultaneous Confidence
Simultaneous Confidence Intervals
Simultaneous Confidence Level
Simultaneous Confidence Set
statistical inference
Sub-Markovian Bounds
Therapeutic Window
Upper Bounds

Product details

  • ISBN 9780367378523
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 05 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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 Fréchet 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.

Chen, John

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