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We consider the problem of estimating functionals of discrete distributions, and focus on a tight (up to universal multiplicative constants for each specific functional) nonasymptotic analysis of the worst case squared error risk of widely used estimators. We apply concentration inequalities to analyze the random fluctuation of these estimators around their expectations and the theory of approximation using positive linear operators to analyze the deviation of their expectations from the true functional, namely their bias. We explicitly characterize the worst case squared error risk incurred by the maximum likelihood estimator (MLE) in estimating the Shannon entropy H(P) = Σ _i=1 ^S -p _i ln p _i , and the power sum F _α (P) = Σ _i=1 ^S p _i ^α , α > 0, up to universal multiplicative constants for each fixed functional, for any alphabet size S ≤ ∞ and sample size n for which the risk may vanish. As a corollary, for Shannon entropy …