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Network constrained covariate coefficient and connection sign estimation

Author

Listed:
  • Matthias Weber

    (Research Center CEFER, Bank of Lithuania & Faculty of Economics and Business Administration, Vilnius University)

  • Jonas Striaukas

    (Universite catholique de Louvain, CORE)

  • Martin Schumacher

    (Institute of Medical Biometry and Statistics, University of Freiburg)

  • Harald Binder

    (Institute of Medical Biometry and Statistics, University of Freiburg)

Abstract

Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings. In particular, an additional network penalty can be added on top of another penalty term, such as a Lasso penalty. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We show detailed simulation results of such an algorithm. The algorithm performs well in a variety of settings. We also briefly describe the R-package that we developed for this purpose, which is publicly available.

Suggested Citation

  • Matthias Weber & Jonas Striaukas & Martin Schumacher & Harald Binder, 2018. "Network constrained covariate coefficient and connection sign estimation," Bank of Lithuania Discussion Paper Series 8, Bank of Lithuania.
    • Handle: RePEc:lie:dpaper:8
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    File URL: https://www.lb.lt/en/publications/no-8-matthias-weber-jonas-striaukas-martin-schumacher-harald-binder-network-constrained-covariate-coefficient-and-connection-sign-estimation
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    More about this item

    Keywords

    network regression; network penalty; connection sign estimation; regularized regression;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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