Conference

On the Contraction Method with Reduced Independence Assumptions

Bibliographic Details
Title: On the Contraction Method with Reduced Independence Assumptions
Authors: Neininger, Ralph, Straub, Jasmin
Contributors: Ralph Neininger and Jasmin Straub
Publisher Information: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2022.
Publication Year: 2022
Subject Terms: Contraction Method, weak Convergence, random Trees, Probabilistic Analysis of Algorithms, ddc:004, Probability Metrics
Description: Recursive sequences of laws of random variables (and random vectors) are considered where an independence assumption which is usually made within the setting of the contraction method is dropped. This restricts the study to sequences which after normalization lead to asymptotic normality. We provide a general univariate central limit theorem which can directly be applied to problems from the analysis of algorithms and random recursive structures without further knowledge of the contraction method. Also multivariate central limit theorems are shown and bounds on rates of convergence are provided. Examples include some previously shown central limit analogues as well as new applications on Fibonacci matchings.
Document Type: Conference object
Article
File Description: application/pdf
Language: English
DOI: 10.4230/lipics.aofa.2022.14
Access URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AofA.2022.14
Rights: CC BY
Accession Number: edsair.dedup.wf.002..a74c8c672dd8c363312ba23bce3430ce
Database: OpenAIRE
Description
DOI:10.4230/lipics.aofa.2022.14