Simulation Study for the Clan of Ancestors in a Perfect Simulation Scheme of a Continuous One-Dimensional Loss Network

Nancy L. Garcia () and Nevena Marić ()
Additional contact information
Nancy L. Garcia: UNICAMP
Nevena Marić: Syracuse University

Methodology and Computing in Applied Probability, 2008, vol. 10, issue 3, 453-469

Abstract: Abstract Perfect simulation of a one-dimensional loss network on ℝ with length distribution π and cable capacity C is performed using the clan of ancestors method. Previous works estimated the region of convergence of this scheme using a domination by a branching process. In this work, we show that the domination by the branching process is far from sharp and that there is room for improvement. Moreover, we derive an empirical relation concerning the critical value using simulation studies on the number of rectangles present in the clan of ancestors.

Keywords: Clan of ancestors; Multitype branching process; Perfect simulation; Primary 93E30; 60G55; Secondary 15A18 (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11009-007-9058-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:10:y:2008:i:3:d:10.1007_s11009-007-9058-y

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-007-9058-y

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().