Atkinson,A.C., Riani,M., Corbellini,A., Perrotta D., and Todorov,V. (2024), Applied Robust Statistics through the Monitoring Approach, Heidelberg: Springer Nature.
This chapter introduces algorithms for the monitoring approach to robust statistical analysis
and compares their performance in the analyses of several sets of data. The Forward Search (FS) is introduced in section 4.1. This algorithm fits subsets of the data of increasing size in such a way that the most outlying observations are included towards the end of the search. To monitor other forms of robust regression, we estimate the parameters using, typically, a grid of 50 values of bdp or eff. Monitoring plots of residuals, parameter estimates and
FileName | Description | Open in MATLAB on line | Jupiter notebook |
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ARregression.m | AR regression data This file creates Figures 4.15-4.22 |
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ARregressionInteractive.m | AR regression data This file creates Figures 4.12-4.14 Note that this file needs interactivity |
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Bank.m | Bank data This file creates Figure 4.34-4.42. |
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BankInteractive.m | Bank data This file creates Figure 4.35 and 4.36. |
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Hawkins.m | Hawkins data This file creates Figure 4.23 and Figures 4.27-4.29 |
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HawkinsInteractive.m | Hawkins data (interactive part). This file creates Figures 4.24-4.26. Note that this file needs interactivity. |
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MentalIllness.m | Contaminated illness data. This file creates Figure 4.43. |
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Stars.m | Stars data. This file creates Figures 4.1-4.4, 4.9-4.11 |
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SurgicalUnit.m | Surgical Unit data. This file creates Figures 4.30-4.33. |
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Wool.m | Wool data. This file creates Figures 4.5-4.8. |
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