A stochastic algorithm approach for the elephant random walk with applications
Li-Xin Zhang

TL;DR
This paper introduces a stochastic algorithm approach to analyze the elephant random walk (ERW), providing Gaussian approximations and limit theorems that deepen understanding of ERW's asymptotic behaviors across various regimes.
Contribution
It links the ERW and response-adaptive designs with recursive stochastic algorithms, deriving new limit theorems and precise laws of the iterated logarithm for ERW.
Findings
Gaussian approximation of ERW and multi-dimensional ERW with random step sizes
Central limit theorem and law of the iterated logarithm for ERW
Chung type laws of the iterated logarithm for ERW with random step sizes
Abstract
The randomized play-the-winner rule (RPW) is a response-adaptive design proposed by Wei and Durham (1978) for sequentially randomizing patients to treatments in a two-treatment clinical trial so that more patients are assigned to the better treatment as the clinical trial goes on. The elephant random walk (ERW) proposed by Schutz and Trimper (2004) is a non-Markovian discrete-time random walk on which has a link to a famous saying that elephants can always remember where they have been. The asymptotic behaviors of RPW rule and ERW have been studied in litterateurs independently, and their asymptotic behaviors are very similar. In this paper, we link RPW rule and ERW with the recursive stochastic algorithm. With the help of a recursive stochastic algorithm, we obtain the Gaussian approximation of the ERW and multi-dimensional varying-memory ERW with random step sizes. By the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDiffusion and Search Dynamics
