There are novel approaches to Robust Linear Programming and Robust Least Squares which convert them into nonsmooth convex optimization problems. We provide similar technique for robust eigenvector problem for stochastic matrices. Such robust formulations are natural for numerous ranking problems. The approach is compared with standard ones, such as PageRank. Numerical optimization algorithms for nonsmooth problems arising are addressed.