Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 71 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 23 tok/s Pro
GPT-5 High 17 tok/s Pro
GPT-4o 111 tok/s Pro
Kimi K2 161 tok/s Pro
GPT OSS 120B 412 tok/s Pro
Claude Sonnet 4 35 tok/s Pro
2000 character limit reached

An alternative to the coupling of Berkes-Liu-Wu for strong approximations (1707.07957v1)

Published 25 Jul 2017 in math.PR

Abstract: In this paper we propose an alternative to the coupling of Berkes, Liu and Wu [1] to obtain strong approximations for partial sums of dependent sequences. The main tool is a new Rosen-thal type inequality expressed in terms of the coupling coefficients. These coefficients are well suited to some classes of Markov chains or dynamical systems, but they also give new results for smooth functions of linear processes.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.