Replace 2023 with 2024

cap10/README.md

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-Atkinson,A.C., Riani,M., Corbellini,A., Perrotta D., and Todorov,V. (2023), Applied Robust Statistics through the Monitoring Approach, Heidelberg: Springer Nature.
+Atkinson,A.C., Riani,M., Corbellini,A., Perrotta D., and Todorov,V. (2024), Applied Robust Statistics through the Monitoring Approach, Heidelberg: Springer Nature.
 
 # Abstract
  In this last chapter, we exhibit the power of the methods described earlier, by analysing five datasets. We start in section 10.2 with the two sets of income data from section 1.4. Without explanatory variables we found the log transformation for the first, with the analysis of the second inconclusive. When explanatory variables are included, and outliers deleted, the square-root transformation is indicated for both. In section 10.4 we analyse 1711 responses to a survey on customer loyalty, in which there are six explanatory variables. Parametric methods lead to $\surd{y}$ as the response, the identification of 41 outliers and a skewed distribution of residuals. RAVAS followed by the FS provides a good approximation to normally distributed errors, when only nine observations are deleted. Despite transformation and outlier detection, the $t$-statistics for the significance of the variables hardly change. Accordingly, in section10.5 we modify 25 observations: monitoring plots reveal the outliers and the results of the RAVAS analysis are close to those for the uncontaminated data. Finally, we analyse the NCI-60 cancer cell data (Chapter 9).

cap9/README.md

@@ -2,7 +2,7 @@
 
 
 ---
-Atkinson,A.C., Riani,M., Corbellini,A., Perrotta D., and Todorov,V. (2023), Applied Robust Statistics through the Monitoring Approach, Heidelberg: Springer Nature.
+Atkinson,A.C., Riani,M., Corbellini,A., Perrotta D., and Todorov,V. (2024), Applied Robust Statistics through the Monitoring Approach, Heidelberg: Springer Nature.
 
 # Abstract
  This chapter considers the choice of explanatory variables to include in the linear predictor $x^T\beta$. We start with models for all of which $p$, the dimension of $\beta$, is $ < n.$ The problem arises specifically when some variables are nearly collinear when the significance of a variable in the model may depend strongly on what other variables are included. Section 9.3.1 derives Mallow's $C_p$ from Akaike's AIC; models with more parameters are penalized. Robustness is provided by the generalized candlestick plot, illustrated by three data analyses. For the rest of the chapter we take $n < p.$