Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data
Journal article, 2016

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.

distributed estimation

regularization

diffusion strategy

mean-squareerror

Missing data

linear regression

Author

Mohammad Reza Gholami

Magnus Jansson

Erik Ström

Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner, Communication Systems

Ali H. Sayed

IIEEE Transactions on Signal and Information Processing over Networks

2373-776X (eISSN)

Vol. 2 3 276-289

Areas of Advance

Information and Communication Technology

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

Probability Theory and Statistics

Signal Processing

DOI

10.1109/TSIPN.2016.2570679

More information

Created

10/7/2017