Proposes an adaptive hybrid estimator for common mean estimation under independent but non-identical symmetric unimodal distributions, with near-optimality guarantees even when only log n / n samples are low-noise.
Define the following random variables: Z1 = sup f ∈K Rn(f ), Z 2 = sup f ∈J Rn(f ) These relations suffice for showing that Z1 < Z2 with constant probability
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Estimating location parameters in entangled single-sample distributions
Proposes an adaptive hybrid estimator for common mean estimation under independent but non-identical symmetric unimodal distributions, with near-optimality guarantees even when only log n / n samples are low-noise.