I have a large number of variables that I'm trying to reduce, and I've stumbled on Kuhn's (2008) suggestion to eliminate variables with zero or near-zero variance:
[Near-zero variance means that the] fraction of unique values over the sample size is low (say 10%) [...] [and the] ratio of the frequency of the most prevalent value to the frequency of the second most prevalent value is large (say around 20). If both of these criteria are true and the model in question is susceptible to this type of predictor, it may be advantageous to remove the variable from the model.
-- Kuhn, M., & Johnson, K. (2013). Applied predictive modeling, New York, NY: Springer.
This makes sense to me, it's been used in other studies, and it would do exactly what I'm hoping for with my data. However, as far as I can tell, Kuhn doesn't provide any justification (either theoretical or empirical) for using this technique, and I can't find any other literature that supports this.
Does anyone know of other sources that demonstrate why this technique works?