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Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
2016 (English)In: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, ISSN 2373-776X, Vol. 2, no 3, 276-289 p.Article in journal (Refereed) Published
Abstract [en]

In many fields, and especially in the medical and social sciences and in recommender systems, data are gathered through clinical studies or targeted surveys. Participants are generally reluctant to respond to all questions in a survey or they may lack information to respond adequately to some questions. The data collected from these studies tend to lead to linear regression models where the regression vectors are only known partially: some of their entries are either missing completely or replaced randomly by noisy values. In this work, assuming missing positions are replaced by noisy values, we examine how a connected network of agents, with each one of them subjected to a stream of data with incomplete regression information, can cooperate with each other through local interactions to estimate the underlying model parameters in the presence of missing data. We explain how to adjust the distributed diffusion strategy through (de)regularization in order to eliminate the bias introduced by the incomplete model. We also propose a technique to recursively estimate the (de)regularization parameter and examine the performance of the resulting strategy. We illustrate the results by considering two applications: one dealing with a mental health survey and the other dealing with a household consumption survey.

Place, publisher, year, edition, pages
IEEE, 2016. Vol. 2, no 3, 276-289 p.
Keyword [en]
Missing data, linear regression, mean-square-error, regularization, distributed estimation, diffusion strategy
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-194280DOI: 10.1109/TSIPN.2016.2570679ISI: 000384248200004OAI: oai:DiVA.org:kth-194280DiVA: diva2:1039567
Note

QC 20161024

Available from: 2016-10-24 Created: 2016-10-21 Last updated: 2016-10-24Bibliographically approved

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