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Generalized monotone additive latent variable models

Collection
  • Cahiers de Recherche HEC; 2010.02
Publication date2010
Abstract

For manifest variables with additive noise and for a given number of latent variables with an assumed distribution, we propose to nonparametrically estimate the association between latent and manifest variables. Our estimation is a two step procedure: first it employs standard factor analysis to estimate the latent variables as theoretical quantiles of the assumed distribution; second, it employs the additive models' backfitting procedure to estimate the monotone nonlinear associations between latent and manifest variables. The estimated fit may suggest a different latent distribution or point to nonlinear associations. We show on simulated data how, based on mean squared errors, the nonparametric estimation improves on factor analysis. We then employ the new estimator on real data to illustrate its use for exploratory data analysis.

Keywords
  • Factor analysis
  • Principal component analysis
  • Nonparametric regression
  • Bartlett's factor scores
  • Dimension reduction
Citation (ISO format)
SARDY, Sylvain, VICTORIA-FESER, Maria-Pia. Generalized monotone additive latent variable models. 2010
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accessLevelPublic
Identifiers
  • PID : unige:23114
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Technical informations

Creation09/10/2012 4:25:00 PM
First validation09/10/2012 4:25:00 PM
Update time03/14/2023 5:41:42 PM
Status update03/14/2023 5:41:42 PM
Last indexation01/16/2024 12:17:19 AM
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