Bootstrap Methods: With Applications in R

This book provides a comprehensive introduction to the bootstrap method, covering point estimation, test theory, multivariate linear regression models, and generalized linear models. It emphasizes the use of bootstrap procedures for goodness-of-fit tests and presents new methods with R implementations. The intended audience is graduate students with prior knowledge in probability theory and mathematical statistics.

Format: Paperback / softback
Length: 256 pages
Publication date: 12 August 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book offers a concise yet thorough introduction to the bootstrap method. In addition to covering classical results in point estimation and test theory, it delves into detailed explanations of multivariate linear regression models and generalized linear models. Special emphasis is placed on utilizing bootstrap procedures to perform goodness-of-fit tests, validating model or distributional assumptions. Furthermore, the book presents novel methods in certain instances.

Motivated by practical applications and accompanied by clear R implementations, each chapter includes a section of exercises and concludes with rigorous proofs for the more mathematically inclined readers. The intended audience consists of graduate students with a solid foundation in probability theory and mathematical statistics.

By leveraging the bootstrap approach, this book empowers readers to effectively analyze and validate statistical models, making it an invaluable resource for researchers and practitioners in the fields of statistics and data analysis.

Weight: 421g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030734824
Edition number: 1st ed. 2021