Optimal Sub-Gaussian Mean Estimation in $\mathbb{R}$

We settle the fundamental problem of estimating the mean of a real-valued distribution in the high probability regime, under the minimal (and essentially necessary) assumption that the distribution has finite but unknown variance: we propose an estimator with convergence tight up to a
$1 +o(1)$
factor. Crucially, in contrast to prior works, our estimator does not require prior knowledge of the variance, and works across the entire gamut of distributions with finite variance, including those without any higher moments. Parameterized by the sample size
$n$
, the failure probability
$\delta$
, and the variance
$\sigma^{2}$
, our estimator has additive accuracy within
$\sigma\cdot(1+o(1))\sqrt{\frac{2\log\frac{1}{\delta}}{n}}$
, which is optimal up to the
$1+o(1)$
term. This asymptotically matches the convergence of the sample mean for the Gaussian distribution with the same variance. Our estimator construction and analysis gives a framework generalizable to other problems, tightly analyzing a sum of dependent random variables by viewing the sum implicitly as a 2-parameter
$\psi$
-estimator, and constructing bounds using mathematical programming and duality techniques.