A Sampling Algorithm for Generating Representative Random Samples given Small Sample Size
A novel algorithm is developed for sampling from discrete probability distributions using the probability proportional to size sampling method, which is a special case of Quota sampling method.
The goal is to devise an efficient sampling algorithm that can be used in stochastic optimization problems -- when there is a need to minimize the sample size.
The basic idea of the proposed algorithm is as follows: For a fixed number of random samples N and either ⌊Nx_i ⌋ or ⌈Nx_i ⌉ total observations for the x_i value; the distribution is determined over [⌊Nx_1 ⌋ ,⌈Nx_1 ⌉ ] ,… ,[⌊Nx_k ⌋ ,⌈Nx_k ⌉ ] that best fits the original distribution. More specifically, all possible distributions over[⌊Nx_1 ⌋ ,⌈Nx_1 ⌉ ] ,… ,[⌊Nx_k ⌋ ,⌈Nx_k ⌉ ]are generated; then the distributions that don’t satisfy the total sample size, N, are excluded. After that the best fit distribution is selected
For further details, kindly read the following paper:
http://www.igi-global.com/article/novel-quota-sampling-algorithm-generating/76350
Cite As
Mohamed Saleh (2024). A Sampling Algorithm for Generating Representative Random Samples given Small Sample Size (https://www.mathworks.com/matlabcentral/fileexchange/36987-a-sampling-algorithm-for-generating-representative-random-samples-given-small-sample-size), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Probability Distributions > Multivariate Distributions > Multivariate Normal Distribution >
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.
Version | Published | Release Notes | |
---|---|---|---|
1.2.0.0 | For further details, kindly read the following paper:
|
||
1.1.0.0 | For further details, kindly read the following paper:
|
||
1.0.0.0 |