Comments (3)
All algorithms implemented in the package preserve the size of the input data set. This means that if your input data set consists of N data points, all resampled data sets will contain exactly N data points.
There are bootstrapping algorithms for N out of M resampling but these are not covered by the package.
from bootstrap.jl.
I see! Thank you for the explanation!
How does this work for models that aren't order dependent? Say we're sampling from some set of data points:
[(1, 0.2), (2, 4.5), (3, 6.5), (5, 19)]
and we resample with replacement to
[(5, 19), (1, 0.2), (5, 19), (2, 4.5)]
Is this not the same as effectively removing the (3, 6.5) data point in your sample, and therefore boiling down to jackknifing?
Further, in the previous case, wouldn't many sample sets be equivalent? e.g., [(5, 19), (1, 0.2), (5, 19), (2, 4.5)] = [(5, 19), (5, 19), (1, 0.2), (2, 4.5)]
from bootstrap.jl.
How does this work for models that aren't order dependent? Say we're sampling from some set of data points:
[(1, 0.2), (2, 4.5), (3, 6.5), (5, 19)]
and we resample with replacement to
[(5, 19), (1, 0.2), (5, 19), (2, 4.5)]
Is this not the same as effectively removing the (3, 6.5) data point in your sample, and therefore boiling down to jackknifing?
No. Jack-knifing here gives you
[(1, 0.2), (2, 4.5), (5, 19)]
while bootstrapping gives you e.g.
[(5, 19), (1, 0.2), (5, 19), (2, 4.5)]
(5, 19) are considered to be 2 independent observations here. The moments and even the number of observations of both samples are different.
Further, in the previous case, wouldn't many sample sets be equivalent? e.g., [(5, 19), (1, 0.2), (5, 19), (2, 4.5)] = [(5, 19), (5, 19), (1, 0.2), (2, 4.5)]
They are equivalent in the sense that a statistic that doesn't care about the order of the observations gives you the same result for both samples.
They are different samples for the bootstrap and fundamental to what makes the bootstrap work.
from bootstrap.jl.
Related Issues (20)
- Time-series bootstrapping HOT 12
- Project.toml HOT 10
- Bootstrap resampling from an arbitrary number of distributions? HOT 3
- Broken compatibility against new release of StatsModels (v0.6.0) HOT 11
- Upgrade `Formulas` to `StatsModels` v0.6.0+ HOT 6
- Interquartile range HOT 6
- Bootstrapping a function with 2 inputs? HOT 8
- Distributions.jl dependency HOT 4
- outdated compat info at General Registry HOT 2
- Confidence Interval Output HOT 1
- Update DataFrames dependency HOT 2
- Feature to retrieve all sampled results so that a histogram can be obtained HOT 6
- Feature Suggestion: Bayesian Bootstrap HOT 2
- Bump Distributions.jl to 0.25 HOT 8
- Request for documentation: Balanced Sampling
- Increased Modularity/Composability
- Exact Bootstrap?
- Allow passing an RNG
- Plase bump `StatsBase` compat bound HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from bootstrap.jl.