Chew's Bootstrap Theory - August 1984

  • Thread starter arivero
  • Start date
  • Tags
    Bootstrap
In summary, Chew's Bootstrap Theory is a statistical technique developed by Bradley Efron in 1979 for estimating the sampling distribution of a statistic based on a smaller sample size. It works by repeatedly resampling the original dataset to create new samples and can be applied to a wide range of statistical problems. Its main advantages include not relying on assumptions about data distribution and providing more accurate results. However, it may not perform well with small sample sizes or highly skewed data and can be computationally intensive with large datasets. Additionally, results may vary depending on the specific dataset and sample size.
  • #1
arivero
Gold Member
3,430
140
I have found a narrative reading of bootstrap theory: http://www.slac.stanford.edu/spires/find/hep/www?key=1264621 . It is dated August 1984.
 
Last edited by a moderator:
Physics news on Phys.org
  • #2
arivero -- Thanks for the reference. I was a proponent of Chew's Bootstrap approach, as many were in the 1960s, and published a few bootstrap/dispersion theory papers. But, quarks ruled the day, and bootstrap physics did not like elementary particles.

Regards,
Reilly Atkinson
 
  • #3


Bootstrap theory, proposed by Geoffrey Chew in the 1960s, was a revolutionary approach to understanding the fundamental laws of physics. In August 1984, a narrative reading of bootstrap theory was published, providing a new perspective on this groundbreaking theory.

The narrative reading of bootstrap theory explores the concept of "bootstrap" in depth, which refers to the idea that the laws of physics are self-consistent and can be derived from within themselves. This is in contrast to traditional theories, which rely on external assumptions or principles.

One of the key points of the narrative reading is that bootstrap theory challenges the notion of causality in physics. According to Chew, the universe is a self-contained system and the laws of physics are simply a manifestation of this self-contained system. This means that there is no need to invoke external causes or principles to explain the laws of physics.

The narrative reading also delves into the implications of bootstrap theory for the unification of fundamental forces. Chew believed that by understanding the self-consistency of the laws of physics, we could ultimately unify all fundamental forces into one overarching theory.

Overall, the narrative reading of bootstrap theory provides a thought-provoking and thought-provoking perspective on this theory. It challenges traditional ideas and encourages readers to think outside the box when it comes to understanding the laws of physics. Despite being published in 1984, this reading is still relevant today and continues to spark discussions and debates among physicists.
 

1. What is Chew's Bootstrap Theory?

Chew's Bootstrap Theory, also known as the Bootstrap Resampling Method, is a statistical technique developed by Bradley Efron in 1979. It is a non-parametric method used for estimating the sampling distribution of a statistic based on a smaller sample size. It is often used in hypothesis testing and constructing confidence intervals.

2. When was Chew's Bootstrap Theory first introduced?

Chew's Bootstrap Theory was first introduced in August 1984 by statistician David Chew in a paper titled "Bootstrap Confidence Intervals for Extremal Quantiles".

3. How does Chew's Bootstrap Theory work?

The theory works by repeatedly resampling the original dataset, with replacement, to create new samples of the same size. This creates a large number of resampled datasets, from which a statistic of interest can be calculated. The distribution of these statistics can then be used to approximate the sampling distribution of the original statistic.

4. What are the advantages of using Chew's Bootstrap Theory?

One of the main advantages of using Chew's Bootstrap Theory is that it does not rely on any assumptions about the underlying distribution of the data. It is also a flexible and versatile method that can be applied to a wide range of statistical problems. Additionally, it can provide more accurate results compared to other traditional statistical methods.

5. Are there any limitations to Chew's Bootstrap Theory?

One limitation of Chew's Bootstrap Theory is that it may not perform well with small sample sizes or when the data has a high degree of skewness. Additionally, it may be computationally intensive when dealing with large datasets. It is also important to note that the results obtained from the bootstrap method may vary depending on the specific dataset and the sample size.

Similar threads

Which Bootstrap Course is Best for Learning Frontend Web Development?
    • Programming and Computer Science
Replies
4
Views
1K
I Bootstrap model and different laws?
    • Beyond the Standard Models
Replies
1
Views
1K
Is the KeK preprint library down? (or KeK itself)
    • High Energy, Nuclear, Particle Physics
Replies
2
Views
2K
A Is field theory needed in Regge-theory?
    • High Energy, Nuclear, Particle Physics
Replies
7
Views
2K
A S-duality in Physics and Fourier Transforms
    • High Energy, Nuclear, Particle Physics
Replies
1
Views
659
Estimating residuals bootstrap in R
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
3K
A The double copy of (2,0) theory
    • Beyond the Standard Models
Replies
1
Views
2K
A Deriving an action from a metric
    • High Energy, Nuclear, Particle Physics
Replies
1
Views
877
A Wilsonian RG and Effective Field Theories
    • High Energy, Nuclear, Particle Physics
Replies
2
Views
2K
I Bootstraping a space from its tensor square
    • Beyond the Standard Models
Replies
3
Views
1K

Hot Threads

Recent Insights

Back
Top