English

Sharp Threshold for the Convergence of Nonstationary Averaging

Probability 2026-03-18 v1

Abstract

We study non-stationary averaging processes, where each term of a sequence is a weighted average of previous terms, namely an+1=j=1npn(j)aja_{n+1} = \sum_{j=1}^n p_n(j) a_j. Our results extend classical theory in two distinct regimes. First, we prove a sharp threshold for convergence in the regime where the weights are bounded between two envelopes (logn)αnpn()(logn)β(\log n)^{-\alpha} \le np_n(\cdot) \leq (\log n)^{\beta}. We show that the sequence necessarily converges when α+β/21\alpha + \beta / 2 \leq 1, while α+β/2>1\alpha + \beta / 2 > 1 the convergence can fail. Second, we study complementary fixed shape regime, when pnp_n is obtained by a fixed limiting density on (0,1)(0,1). We show that under mild regularity assumptions, the sequence converges.

Keywords

Cite

@article{arxiv.2603.16678,
  title  = {Sharp Threshold for the Convergence of Nonstationary Averaging},
  author = {Saba Lepsveridze and Elchanan Mossel},
  journal= {arXiv preprint arXiv:2603.16678},
  year   = {2026}
}

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29 pages