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Minimum ϕ -Divergence Estimation in Constrained Latent Class Models for Binary Data.

A Felipe1, P Miranda2, L Pardo1

  • 1Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, 28040 , Madrid, Spain.

Psychometrika
|March 1, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces minimum divergence estimators for latent class models, offering a robust alternative to maximum likelihood estimation for binary data analysis. Simulation results compare their efficiency, especially in smaller sample sizes.

Keywords:
asymptotic distributionlatent class modelsmaximum-likelihood estimatorminimum phi-divergence estimator

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Area of Science:

  • Statistics
  • Machine Learning
  • Psychometrics

Background:

  • Latent class models are widely used for analyzing binary data.
  • Maximum likelihood estimation (MLE) is a common but sometimes limited approach.
  • Alternative estimators are needed for improved robustness and efficiency.

Purpose of the Study:

  • Introduce and analyze minimum divergence estimators as an alternative to MLE.
  • Extend MLE concepts to a broader class of estimators.
  • Evaluate the performance of these estimators in latent class models.

Main Methods:

  • Develop asymptotic properties of minimum divergence estimators for binary latent class models.
  • Conduct simulation studies to compare estimator performance.
  • Assess efficiency and robustness against MLE.

Main Results:

  • Minimum divergence estimators are a natural extension of MLE.
  • Asymptotic properties for these estimators in binary latent class models are established.
  • Simulation results indicate potential advantages in specific scenarios.

Conclusions:

  • Minimum divergence estimators provide a viable and potentially more robust alternative to MLE.
  • The study contributes theoretical properties and empirical comparisons for these estimators.
  • Further research can explore applications in diverse latent class modeling contexts.