Python Scripts for Simulating, Analyzing, and Evaluating Dispersion Estimators

This presentation involves simulation processes, data analysis, and comparisons of conventional and proposed dispersion estimators. All the parameters and metrics used are based on the methodology presented in the article titled "Statistical Mirroring: A Robust Method for Statistical Dispersion Estimation" authored by Kabir Bindawa Abdullahi in 2024. For further details, you can follow the publication of the paper submitted to MethodsX Elsevier Publishing.

The presentation is categorized into six (6) comprehensive sections:

* Section 1: Importation of useful libraries and other implemented codes.
* Section 2: Definition of basic functions used in the simulation process, data analysis, and evaluation of estimator performance.
* Section 3: Presentation of the simulation process, data analysis, evaluation, and comparison of estimators under normal distribution.
* Section 4i: Presentation of the simulation process, data analysis, evaluation, and comparison of some dispersion estimators under Gaussian mixture model distribution (I).
* Section 4ii: Presentation of the simulation process, data analysis, evaluation, and comparison of some dispersion estimators under Gaussian mixture model distribution (II).
* Section 5: Presentation of the data analysis and comparison of dispersion estimators under different temperature measurement scales using a real-life dataset.
* Section 6: Set duplication-invariance of dispersion estimators."

NOTE: Data users can choose between downloading the complete folder, which includes processed files and results, or opting for the alternative of downloading the Python script. In the latter scenario, users MUST uncomment and rerun the script to generate fresh files and results. For the former option, users CAN uncomment and rerun the script to update and obtain new results.