Bootstrap Methods and their Application Book Summary - Bootstrap Methods and their Application Book explained in key points

Bootstrap Methods and their Application summary

A. C. Davison

Brief summary

Bootstrap Methods and their Application by A. C. Davison provides a comprehensive introduction to the bootstrap technique, a powerful statistical tool for assessing the accuracy of sample estimates and for making inferences about population parameters.

Give Feedback
Table of Contents

    Bootstrap Methods and their Application
    Summary of key ideas

    Understanding Bootstrap Methods

    In Bootstrap Methods and Their Application by A. C. Davison, we delve into the world of bootstrap methods, a set of statistical techniques used for estimating the sampling distribution of a statistic by resampling with replacement from the original data. The book begins with a comprehensive introduction to the concept of bootstrap, providing a historical background and an explanation of its theoretical underpinnings.

    We learn that the bootstrap method is particularly useful when the underlying distribution of a statistic is unknown or difficult to determine. By resampling from the observed data, we can approximate the distribution of the statistic of interest. The book carefully explains the mechanics of the bootstrap process, including its various forms such as the non-parametric, parametric, and model-based bootstrap.

    Bootstrapping in Practice

    After establishing a solid foundation, Bootstrap Methods and Their Application moves on to the practical aspects of bootstrapping. The author discusses the application of bootstrap methods in different statistical scenarios, such as point estimation, hypothesis testing, and constructing confidence intervals. We learn how to use bootstrap to estimate standard errors, derive confidence intervals for complex statistics, and test hypotheses in situations where traditional methods are not applicable.

    The book then explores the use of bootstrap techniques in more advanced statistical models. It covers linear regression, time series analysis, and survival analysis, demonstrating how bootstrap methods can be adapted to handle these complex models. The author emphasizes the importance of understanding the underlying assumptions and limitations when applying bootstrapping in these scenarios.

    Specialized Bootstrap Techniques

    As we progress through Bootstrap Methods and Their Application, we encounter specialized bootstrap techniques designed for specific statistical problems. For instance, the author introduces the concept of the wild bootstrap, a method tailored for autoregressive models with heteroscedastic errors. We also learn about the stationary bootstrap, a technique developed for time series data with non-constant variance.

    In addition to time series, the book discusses the application of bootstrap methods in spatial statistics. We explore how bootstrapping can be used to estimate the uncertainty in spatial statistics, such as spatial autocorrelation and variograms. The author provides examples to illustrate the implementation of these techniques in spatial data analysis.

    Challenges and Future Directions

    In the latter part of the book, Bootstrap Methods and Their Application addresses some of the challenges and criticisms associated with bootstrap techniques. We learn about potential pitfalls, such as the impact of data dependencies and the choice of resampling method, and how researchers can mitigate these issues. The author also discusses the limitations of bootstrap methods and situations where alternative approaches may be more appropriate.

    In conclusion, Bootstrap Methods and Their Application by A. C. Davison offers a comprehensive exploration of bootstrap methods, from their theoretical foundations to their practical application in a wide range of statistical problems. The book equips readers with a deep understanding of how and when to use bootstrapping, making it an invaluable resource for statisticians, researchers, and data analysts seeking to harness the power of resampling techniques in their work.

    Give Feedback
    How do we create content on this page?
    More knowledge in less time
    Read or listen
    Read or listen
    Get the key ideas from nonfiction bestsellers in minutes, not hours.
    Find your next read
    Find your next read
    Get book lists curated by experts and personalized recommendations.
    Shortcasts
    Shortcasts New
    We’ve teamed up with podcast creators to bring you key insights from podcasts.

    What is Bootstrap Methods and their Application about?

    Bootstrap Methods and their Application by A. C. Davison provides a comprehensive introduction to the bootstrap method, a powerful statistical tool used for assessing the accuracy of sample estimates and making inferences about population parameters. The book covers the theoretical foundations of bootstrap, its practical implementation, and its application in various fields such as finance, biology, and environmental science. It is a valuable resource for researchers and practitioners interested in understanding and utilizing this resampling technique.

    Bootstrap Methods and their Application Review

    Bootstrap Methods and their Application (1997) is a comprehensive guide on statistical methods that has a practical approach beneficial for both beginners and experts alike. Here's why this book stands out:
    • Featuring in-depth explanations and numerous examples, it clarifies complex concepts and techniques, fostering a deep understanding of bootstrap methods.
    • With a strong focus on practical applications in various fields, from medicine to finance, it demonstrates the versatility and relevance of these methods.
    • The book's engaging approach to statistical analysis keeps readers intrigued and connected, ensuring a dynamic and enriching reading experience.

    Who should read Bootstrap Methods and their Application?

    • Statisticians and data analysts looking to understand and apply bootstrap methods

    • Researchers and academics in the fields of social sciences, economics, and business

    • Professionals in industries such as finance, healthcare, and technology who want to improve their statistical inference and model validation techniques

    About the Author

    A. C. Davison is a renowned statistician and author who has made significant contributions to the field of bootstrap methods. He has published extensively on the topic, with his book 'Bootstrap Methods and their Application' being a seminal work in the area. Davison's research focuses on developing and refining statistical techniques for resampling and inference. His work has had a profound impact on the way researchers analyze and interpret data. In addition to his book, Davison has also authored numerous influential journal articles and book chapters.

    Categories with Bootstrap Methods and their Application

    People ❤️ Blinkist 
    Sven O.

    It's highly addictive to get core insights on personally relevant topics without repetition or triviality. Added to that the apps ability to suggest kindred interests opens up a foundation of knowledge.

    Thi Viet Quynh N.

    Great app. Good selection of book summaries you can read or listen to while commuting. Instead of scrolling through your social media news feed, this is a much better way to spend your spare time in my opinion.

    Jonathan A.

    Life changing. The concept of being able to grasp a book's main point in such a short time truly opens multiple opportunities to grow every area of your life at a faster rate.

    Renee D.

    Great app. Addicting. Perfect for wait times, morning coffee, evening before bed. Extremely well written, thorough, easy to use.

    4.7 Stars
    Average ratings on iOS and Google Play
    36 Million
    Downloads on all platforms
    10+ years
    Experience igniting personal growth
    Powerful ideas from top nonfiction

    Try Blinkist to get the key ideas from 7,500+ bestselling nonfiction titles and podcasts. Listen or read in just 15 minutes.

    Get started

    Bootstrap Methods and their Application FAQs 

    What is the main message of Bootstrap Methods and their Application?

    The main message of Bootstrap Methods and their Application focuses on the practical application of bootstrap methods in statistical analysis.

    How long does it take to read Bootstrap Methods and their Application?

    Reading Bootstrap Methods and their Application takes a few hours. The Blinkist summary can be read in minutes.

    Is Bootstrap Methods and their Application a good book? Is it worth reading?

    Bootstrap Methods and their Application is worth reading for its clear explanations and practical insights into using bootstrap methods effectively.

    Who is the author of Bootstrap Methods and their Application?

    The author of Bootstrap Methods and their Application is A. C. Davison.

    What to read after Bootstrap Methods and their Application?

    If you're wondering what to read next after Bootstrap Methods and their Application, here are some recommendations we suggest:
    • Big Data by Viktor Mayer-Schönberger and Kenneth Cukier
    • Physics of the Future by Michio Kaku
    • On Intelligence by Jeff Hawkins and Sandra Blakeslee
    • Brave New War by John Robb
    • Abundance# by Peter H. Diamandis and Steven Kotler
    • The Signal and the Noise by Nate Silver
    • You Are Not a Gadget by Jaron Lanier
    • The Future of the Mind by Michio Kaku
    • The Second Machine Age by Erik Brynjolfsson and Andrew McAfee
    • Out of Control by Kevin Kelly