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. (2025),

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

A YouTube video summarizing the contents of the book can be found at the link below
Summary
Additional YouTube videos can be found inside the individual chapters.

1. Introduction and the Grand Plan [open dir]

Introduction and the Grand Plan

2. Introduction to M-Estimation for Univariate Samples [open dir]

Introduction to M-Estimation for Univariate Samples PART I

Introduction to M-Estimation for Univariate Samples PART II

3. Robust Estimators in Multiple Regression [open dir]

Analysis of the AR regression data

4. The Monitoring Approach in Multiple Regression [open dir]

Outlier detection with the forward search (Sections 4.1-4.5 and 4.9.5)

Analysis of the Hawkins data (Section 4.9.4)

Analysis of the bank data (Section 4.10)

5. Practical Comparison of the Different Estimators [open dir]

6. Transformations [open dir]

Transformation of the response

7. Non-parametric Regression [open dir]

Non parametric transformations (part I)

Non parametric transformations (part II)

8. Extensions of the Multiple Regression Model [open dir]

Robust Bayesian regression (Sections 8.1- 8.3)

Heteroskedastic regression (Section 8.4)

9. Model selection [open dir]

Variable Selection Mallow's Cp and the generalized candlestick plot (Section 9.3)

10. Some Robust Data Analyses [open dir]

Income data 1: regression analysis (Section 10.2)

Income data 2: regression analysis (Section 10.3)

Analysis of the customer loyalty data (Section 10.4)

Analysis of the modified customer loyalty data (Section 10.5)

Analysis of the NCI60 Cancer Cell Panel Data (Part II, Section 10.6)

Appendix. Solution to the Exercises [open dir]

Analysis of the heart rate data (Exercise 10.1)

Analysis of the auto mpg data (Exercise 10.4)

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 = {2025}  
}

Coding Environment