Two-Sample Rank Statistics Assume we got two models, either regression or RF or whatever. I'm trying to observe the relative importance of each feature by using ranks. That is, if we look at a regression model, we have a t-test (or z-test) for the hypothesis $\beta_j=0$ from which we can obtain p-values, then we can order the features according to their p-values. In RFs we've got Gini importance, you get the point.
Given that I have two ranking vectors (for the sake of simplicity, let's assume they both originate from regression models and relate to the same covariates), how can I compare them? Is there any known statistic?

I look at this question as one of trying to assess the reliability or agreement of the two models in terms of how they rate model features. A simple method for assessing agreement would be to compute the correlation (Pearson, Spearman, Kendall, etc.) between the two vectors of ranks.
The correlation coefficient between the two could provide a single value that would provide an assessment of their concordance.
Other agreement-oriented statistics such as Krippendorf's $\alpha$ could also be useful in this case as well but penalizes deviations across methods differently than would any of the correlation coefficients.  Give the linked Wikipedia page a look with attention to the difference function for ordinal data (most applicable to rank vectors like discussed here).
