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Nonparametric estimation of a latent variable model

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  • Kelava, Augustin
  • Kohler, Michael
  • Krzyżak, Adam
  • Schaffland, Tim Fabian

Abstract

In this paper a nonparametric latent variable model is estimated without specifying the underlying distributions. The main idea is to estimate in a first step a common factor analysis model under the assumption that each manifest variable is influenced by at most one of the latent variables. In a second step nonparametric regression is used to analyze the relation between the latent variables. Theoretical results concerning consistency of the estimates are presented, and the finite sample size performance of the estimates is illustrated by applying them to simulated data.

Suggested Citation

  • Kelava, Augustin & Kohler, Michael & Krzyżak, Adam & Schaffland, Tim Fabian, 2017. "Nonparametric estimation of a latent variable model," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 112-134.
    • Handle: RePEc:eee:jmvana:v:154:y:2017:i:c:p:112-134
    DOI: 10.1016/j.jmva.2016.10.006
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    References listed on IDEAS

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