Abstract:We address the classical problem of constructing confidence intervals (CIs) for the mean of a distribution, given \(N\) i.i.d. samples, such that the CI contains the true mean with probability at least \(1 - \delta\), where \(\delta \in (0,1)\). We characterize three distinct learning regimes based on the minimum achievable limiting width of any CI as the sample size \(N_{\delta} \to \infty\) and \(\delta \to 0\). In the first regime, where \(N_{\delta}\) grows slower than \(\log(1/\delta)\), the limiting width of any CI equals the width of the distribution's support, precluding meaningful inference. In the second regime, where \(N_{\delta}\) scales as \(\log(1/\delta)\), we precisely characterize the minimum limiting width, which depends on the scaling constant. In the third regime, where \(N_{\delta}\) grows faster than \(\log(1/\delta)\), complete learning is achievable, and the limiting width of the CI collapses to zero, converging to the true mean. We demonstrate that CIs derived from concentration inequalities based on Kullback--Leibler (KL) divergences achieve asymptotically optimal performance, attaining the minimum limiting width in both sufficient and complete learning regimes for distributions in two families: single-parameter exponential and bounded support. Additionally, these results extend to one-sided CIs, with the width notion adjusted appropriately. Finally, we generalize our findings to settings with random per-sample costs, motivated by practical applications such as stochastic simulators and cloud service selection. Instead of a fixed sample size, we consider a cost budget \(C_{\delta}\), identifying analogous learning regimes and characterizing the optimal CI construction policy.
From: Vikas Deep [view email]
[v1]
Fri, 31 Jan 2025 13:31:43 UTC (239 KB)
[v2]
Thu, 16 Oct 2025 10:24:32 UTC (76 KB)
[v3]
Wed, 1 Apr 2026 08:43:40 UTC (80 KB)
[v4]
Thu, 11 Jun 2026 03:57:42 UTC (83 KB)
[v5]
Mon, 10 Aug 2026 02:49:10 UTC (83 KB)
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