Nonparametric estimation of a latent variable model

نویسندگان

  • Augustin Kelava
  • Michael Kohler
  • Adam Krzyzak
  • Tim Fabian Schaffland
چکیده

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. AMS classification: Primary 62G08; secondary 62G20.

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عنوان ژورنال:
  • J. Multivariate Analysis

دوره 154  شماره 

صفحات  -

تاریخ انتشار 2017