My continuous dependent variable has a lot of error in it. Hence, I was thinking of discretizing it, to reduce the error for my modeling effort. Hence, I found this para from the "Applied predictive modeling" (by Max Kuhn & Kjell Johnson, 2013) to be relevant:

"A second common reason for wanting to categorize a continuous response is that the scientist may believe that the continuous response contains a high degree of error, so much so that only the response values in either extreme of the distribution are likely to be correctly categorized. If this is the case, then the data can be partitioned into three categories, where data in either extreme are classified generically as positive and negative, while the data in the midrange are classified as unknown or indeterminate. The middle category can be included as such in a model (or specifically excluded from the model tuning process) to help the model more easily discriminant between the two categories."

(See section 20.4, "Discretizing Continuous Outcomes", at the end; pg 533).

My question to you is: Can you please give me two to three good references (ie, journal papers) where dependent variable discretization has been justified in this way? Specifically, I am interested in the boldface part above: where the middle category has been excluded from the model tuning process.

Thanks in advance! R.