Robust variable selection and distributed inference using t-based estimators for large-scale data

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In this paper, we address the problem of performing robust statistical inference for large-scale data sets whose volume and dimensionality maybe so high that distributed storage and processing is required. Here, the large-scale data are assumed to be contaminated by outliers and exhibit sparseness. We propose a distributed and robust two-stage statistical inference method. In the first stage, robust variable selection is done by exploiting t-Lasso to find the sparse basis in each node with distinct subset of data. The selected variables are communicated to a fusion center (FC) in which the variables for the complete data are chosen using a majority voting rule. In the second stage, confidence intervals and parameter estimates are found in each node using robust t-estimator combined with bootstrapping and then combined in FC. The simulation results demonstrate the validity and reliability of the algorithm in variable selection and constructing confidence intervals even if the estimation problem in the subsets is slightly underdetermined.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEURASIP – European Association For Signal Processing
Number of pages5
ISBN (Electronic)978-9-0827-9705-3
Publication statusPublished - 2020
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020
Conference number: 28

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO


  • Bootstrap
  • High-dimensional
  • Large-scale data
  • Robust
  • Sparse
  • Statistical inference
  • Variable selection


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