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**Harvard**

Gholami, M., Jansson, M., Ström, E. och Sayed, A. (2016) *Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data*.

** BibTeX **

@article{

Gholami2016,

author={Gholami, Mohammad Reza and Jansson, Magnus and Ström, Erik G. and Sayed, Ali H.},

title={Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data},

journal={IEEE Transactions on Signal and Information Processing over Networks},

issn={2373-776X},

volume={2},

issue={3},

pages={276-289},

abstract={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.
},

year={2016},

keywords={Missing data, linear regression, mean-squareerror, regularization, distributed estimation, diffusion strategy},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 240288

A1 Gholami, Mohammad Reza

A1 Jansson, Magnus

A1 Ström, Erik G.

A1 Sayed, Ali H.

T1 Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

YR 2016

JF IEEE Transactions on Signal and Information Processing over Networks

SN 2373-776X

VO 2

IS 3

SP 276

OP 289

AB 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.

LA eng

LK http://dx.doi.org/10.1109/TSIPN.2016.2570679

LK http://publications.lib.chalmers.se/records/fulltext/240288/local_240288.pdf

OL 30