Add quantile regression book citation (#892)

Author: lona-k

book/book.bib

@@ -1447,3 +1447,13 @@ @article{yu_quantile_2003
     volume = {52},
     year = {2003},
 }
+@book{koenker_quantile_2005,
+    address = {Cambridge},
+    series = {Econometric Society Monographs},
+    title = {Quantile Regression},
+    isbn = {978-0-521-84573-1},
+    publisher = {Cambridge University Press},
+    author = {Koenker, Roger},
+    year = {2005},
+    doi = {10.1017/CBO9780511754098},
+}

book/chapters/chapter13/beyond_regression_and_classification.qmd

@@ -1031,7 +1031,7 @@ ggplot(plot_data, aes(x = x, y = loss, color = tau)) +
 But note: While many ML models based on empirical risk minimization use the pinball loss for estimating quantiles, some model classes might work differently.
 However, since the underlying training procedure of a model is external to `mlr3`, we are more concerned with resampling and evaluating quantile regression models.
 This works in exactly the same manner as for other tasks.
-Because we provide only a brief overview of quantile regression, we recommend @yu_quantile_2003 if you are interested in a methodological introduction to the topic.
+Because we provide only a brief overview of quantile regression, we recommend @yu_quantile_2003 if you are interested in a methodological introduction to the topic and @koenker_quantile_2005 for a more expansive treatment of quantile regression.
 
 ### Synthetic data set generation {#sec-data-generation}