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.
Furthermore, by Lemma 6(i) above, we know that R(fx,r 1)≥ R(fr2,r 1) when∥x∥2≤ r2
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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.