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So I will only encounter problems if the standard deviation of X or Y is small relative to it's respective mean? Is there a rule of thumb for this level? If I do not expect my data to follow this pattern, then is the "naive" algorithm is sufficient?
For the same reasons that regularized regression often outperforms least squares out of sample. I think that averaging ridge coefficients should work after applying some rule of thumb to the lasso results for feature selection (i.e., intersection, union, or some other threshold for min % of non-zero coefficient selection per feature)
