Fast parameter estimation in loss tomography for networks of general topology

DSpace/Manakin Repository

Fast parameter estimation in loss tomography for networks of general topology

Citable link to this page

 

 
Title: Fast parameter estimation in loss tomography for networks of general topology
Author: Deng, Ke; Li, Yang; Zhu, Weiping; Liu, Jun

Note: Order does not necessarily reflect citation order of authors.

Citation: Deng, Ke, Yang Li, Weiping Zhu, and Jun S. Liu. 2016. “Fast Parameter Estimation in Loss Tomography for Networks of General Topology.” The Annals of Applied Statistics 10 (1) (March): 144–164. doi:10.1214/15-aoas883.
Full Text & Related Files:
Abstract: As a technique to investigate link-level loss rates of a computer network with low operational cost, loss tomography has received considerable attentions in recent years. A number of parameter estimation methods have been proposed for loss tomography of networks with a tree structure as well as a general topological structure. However, these methods suffer from either high computational cost or insufficient use of information in the data. In this paper, we provide both theoretical results and practical algorithms for parameter estimation in loss tomography. By introducing a group of novel statistics and alternative parameter systems, we find that the likelihood function of the observed data from loss tomography keeps exactly the same mathematical formulation for tree and general topologies, revealing that networks with different topologies share the same mathematical nature for loss tomography. More importantly, we discover that a reparametrization of the likelihood function belongs to the standard exponential family, which is convex and has a unique mode under regularity conditions. Based on these theoretical results, novel algorithms to find the MLE are developed. Compared to existing methods in the literature, the proposed methods enjoy great computational advantages.
Published Version: doi:10.1214/15-AOAS883
Other Sources: https://arxiv.org/abs/1510.07158
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33983363
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters