Moving Image Conference Object

Structural Equation Models as Computation Graphs

Author(s) / Creator(s)

van Kesteren, Erik-Jan
Oberski, Daniel

Abstract / Description

Structural equation modeling (SEM) is a popular tool in the social and behavioural sciences, where it is being applied to ever more complex data types. The high-dimensional data produced by modern sensors, brain images, or (epi)genetic measurements require variable selection using parameter penalization; experimental models combining disparate data sources benefit from regularization to obtain a stable result; and genomic SEM or network models lead to alternative objective functions. With each proposed extension, researchers currently have to completely reformulate SEM and its optimization algorithm -- a challenging and time-consuming task. In this talk, I consider each SEM as a computation graph, a flexible method of specifying objective functions borrowed from the field of deep learning. When combined with state-of-the-art optimizers, our computation graph approach can extend SEM without the need for bespoke software development. I show that both existing and novel SEM improvements follow naturally from our approach. To demonstrate, I discuss least absolute deviation estimation and penalized SEM. By applying computation graphs to SEM, we hope to greatly accelerate the process of developing SEM techniques, paving the way for new applications.

Persistent Identifier

Date of first publication

2019-10-16

Publisher

ZPID (Leibniz Institute for Psychology Information)

Citation

Van Kesteren, E.-J., & Oberski, D. (2019, October 16). Structural Equation Models as Computation Graphs. ZPID (Leibniz Institute for Psychology Information). https://doi.org/10.23668/PSYCHARCHIVES.2634
  • Author(s) / Creator(s)
    van Kesteren, Erik-Jan
  • Author(s) / Creator(s)
    Oberski, Daniel
  • PsychArchives acquisition timestamp
    2019-10-29T14:21:03Z
  • Made available on
    2019-10-29T14:21:03Z
  • Date of first publication
    2019-10-16
  • Abstract / Description
    Structural equation modeling (SEM) is a popular tool in the social and behavioural sciences, where it is being applied to ever more complex data types. The high-dimensional data produced by modern sensors, brain images, or (epi)genetic measurements require variable selection using parameter penalization; experimental models combining disparate data sources benefit from regularization to obtain a stable result; and genomic SEM or network models lead to alternative objective functions. With each proposed extension, researchers currently have to completely reformulate SEM and its optimization algorithm -- a challenging and time-consuming task. In this talk, I consider each SEM as a computation graph, a flexible method of specifying objective functions borrowed from the field of deep learning. When combined with state-of-the-art optimizers, our computation graph approach can extend SEM without the need for bespoke software development. I show that both existing and novel SEM improvements follow naturally from our approach. To demonstrate, I discuss least absolute deviation estimation and penalized SEM. By applying computation graphs to SEM, we hope to greatly accelerate the process of developing SEM techniques, paving the way for new applications.
    en
  • Citation
    Van Kesteren, E.-J., & Oberski, D. (2019, October 16). Structural Equation Models as Computation Graphs. ZPID (Leibniz Institute for Psychology Information). https://doi.org/10.23668/PSYCHARCHIVES.2634
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/2253
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.2634
  • Language of content
    eng
  • Publisher
    ZPID (Leibniz Institute for Psychology Information)
    en
  • Is part of
    ZPID-Kolloquium 2019, Trier, Germany
  • Is related to
    https://doi.org/10.23668/psycharchives.2623
  • Is related to
    https://arxiv.org/abs/1905.04492v2
  • Dewey Decimal Classification number(s)
    150
  • Title
    Structural Equation Models as Computation Graphs
    en
  • DRO type
    movingImage
    en
  • DRO type
    conferenceObject
    en
  • Visible tag(s)
    ZPID video portal
    en
  • Visible tag(s)
    ZPID Conferences and Workshops