Applied Robust Statistics through the Monitoring Approach: Applications in Regression

GitHub repo of the forthcoming book "Applied Robust Statistics through the Monitoring Approach: Applications in Regression" Heidelberg: Springer Nature. by Atkinson,A.C., Riani,M., Corbellini,A., Perrotta D., and Todorov,V. (2024),

Reproducible Research (run in MATLAB on line or see Jupyter notebook file with attached output)

All the figures and tables in the books can be reproduced. For each Chapter each .m file can be run in MATLAB on line click on the Run in MATLAB on line button. Moreover each .m file has the corresponding .ipynb file where it is possible to see the preview of the output the .m file generates.

All the README.m files in each Chapter have been automatically created
by FSDA function [m2ipynb]

Table of Contents and Code Notebooks

1. Introduction and the Grand Plan [open dir]
2. Introduction to M-Estimation for Univariate Samples[open dir]
3. Robust Estimators in Multiple Regression [open dir]
4. The Monitoring Approach in Multiple Regression [open dir]
5. Practical Comparison of the Different Estimators [open dir]
6. Transformations [open dir]
7. Non-parametric Regression [open dir]
8. Extensions of the Multiple Regression Model [open dir]
9. Model selection [open dir]
10. Some Robust Data Analyses [open dir]

Appendix. Solution to the Exercises [open dir]

Code by dataset

In the book there are datasets which are used in different Chapters. Here you can find the link to the folder which contains the complete analysis of these datasets

Analysis by dataset [open dir]

Links

$$ Springer Verlag $$ $$ Amazon $$

@book{ARCPT2024,  
address = {UK},  
author = {Atkinson, A. C. and Riani, M. and Corbellini, A. and Perrotta, D. and Todorov, V},  
isbn = {XXX-XXXXXX},   
publisher = {Heidelberg: Springer Nature},  
title = {Applied Robust Statistics through the Monitoring Approach, Applications in Regression},  
year = {2024}  
}

Coding Environment