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Distributed regression estimation with incomplete data in multi-agent networks

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Abstract

In this paper, distributed regression estimation problem with incomplete data in a time-varying multi-agent network is investigated. Regression estimation is carried out based on local agent information with incomplete in the non-ignorable mechanism. By virtue of gradient-based design and adaptive filter, a distributed algorithm is proposed to deal with a regression estimation problem with incomplete data. With the help of convex analysis and stochastic approximation techniques, the exact convergence is obtained for the proposed algorithm with incomplete data and a jointly-connected multi-agent topology. Moreover, online regret analysis is also given for real-time learning. Then, simulations for the proposed algorithm are also given to demonstrate how it can solve the estimation problem in a distributed way, even when the network configuration is time-varying.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (Grant No. 2016YFB0901902) and National Natural Science Foundation of China (Grant Nos. 61573344, 61333001, 61374168).

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Correspondence to Yinghui Wang.

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Cite this article

Wang, Y., Lin, P. & Hong, Y. Distributed regression estimation with incomplete data in multi-agent networks. Sci. China Inf. Sci. 61, 092202 (2018). https://doi.org/10.1007/s11432-016-9173-8

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Keywords

  • multi-agent systems
  • time-varying network
  • estimation with incomplete data
  • online learning
  • stochastic approximation