https://github.com/bm777/bootstrap-method-for-error-estimation
Bootstrap method to estimate error like standard error or confident interval
https://github.com/bm777/bootstrap-method-for-error-estimation
error-estimation error-estimators standard
Last synced: 4 months ago
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Bootstrap method to estimate error like standard error or confident interval
- Host: GitHub
- URL: https://github.com/bm777/bootstrap-method-for-error-estimation
- Owner: bm777
- Created: 2021-01-30T17:30:42.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-03-06T13:59:58.000Z (almost 5 years ago)
- Last Synced: 2025-01-05T07:45:28.827Z (about 1 year ago)
- Topics: error-estimation, error-estimators, standard
- Language: Python
- Homepage:
- Size: 3.05 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# bootstrap-method-for-error-estimation
#### Tools used:
Python 3 on Ubuntu 18.04 and Core i7-9750 (CPU @2.60GHz x 12).
#### Type of data
The main dataset is made up by IQ. list of 40 sample and each between 30 and 170.
#### Result of computing
### How to use the Class
Tasks you have TO DO.
- [x] Step [1] : DATASET GENERATION
```
estim = Estimator(default=40) # 40 is the default lenght of the set
counter = 0 # important for the next step
maximum = 10000 # you have to set your max number for iteration
```
- [x] Step [5] : REPEAT STEP 2 though 4 10000 times (start of loop)
```
while counter < maximum
# step [2,3] := RANDOM PICK UP FOR ALL SAMPLES(default=40)
estim.pick()
# step [4] := CAULCULATE our SAMPLE STATISTIC(mean and median)
estim.mean_median[0].append(estim.calculate_mean(estim.pick()))
estim.mean_median[1].append(estim.calculate_median(estim.pick()))
```
- [x] Step [6] := CAULCULATE STANDARD DEVIATION of distribution of the 10,000 means and medians
```
estim.calculate_SD(estim.mean_median[0]) # SD of mean
estim.calculate_SD(estim.mean_median[1]) # SD of median
```
- [x] Step [7] := CAULCULATE 2.5th and 97.5th centiles of the 10,000 means and medians
```
mean_of_mean = estim.calculate_mean(estim.mean_median[0])
mean_of_median = estim.calculate_mean(estim.mean_median[1])
sd_of_mean = estim.calculate_SD(estim.mean_median[0])
sd_of_median = estim.calculate_SD(estim.mean_median[1])
estim.small_centile([mean_of_mean, sd_of_mean]) # 2.5th centile of 10000 means
estim.big_centile([mean_of_mean, sd_of_mean]) # 97.5th centile of 10000 means
estim.small_centile([mean_of_median , sd_of_median]) # 2.5th centile of 10000 medians
estim.big_centile([mean_of_median , sd_of_median]) # 97.5th centile of 10000 medians
```
#### Credit to this.
This [LINK](https://www.dummies.com/education/science/biology/the-bootstrap-method-for-standard-errors-and-confidence-intervals/) help me to understand the fundamental notion of bootstrap method.