A note on 'Testing the number of components in a normal mixture'

By Neal O. Jeffries. 2003

76 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e7d0c396f2732fc22b49afb2aafe130061f3206f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Alexandre J. S. Morin
2. Wen Luo
3. Qi Chen
4. T. Asparouhov
5. K. Nylund
6. S. Reise
7. James M. Henson
8. Kevin Kim

In Statistics and Probability

1. Alexandre J. S. Morin
2. B. Muthén
3. Wen Luo
4. Qi Chen
5. T. Asparouhov
6. Herbert W. Marsh
7. K. Nylund
8. S. Reise

Citations in the corpus

• Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data (2004, background): “The Lo-Mendell-Rubin approach has been criticized (Jeffries, 2003), although it is unclear to which extent the critique affects its use in practice. The Lo-Mendell-Rubin likelihood ratio test (LMR LRT) avoids a classic problem of chisquare testing based on likelihood ratios. This concerns models tha”
• Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics (2007, result): “Furthermore, Jeffries (2003) noted that the assumptions presented in Lo et al. that underlie these statistics are typically not met with traditional mixture models, and the results of his simulations revealed a bias in the VLMR that did not decrease with larger samples (n = 5,000).”
• Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study (2007, background): “Jeffries (2003) claimed that there is a flaw in the mathematical proof of the LMR test for normal outcomes.”
• The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored (2017, background): “It should be noted that the conditions for the ALMR theorem are not generally satisfied in the context of mixture models (Jeffries, 2003); nevertheless, the ALMR has been shown to be effective in recovering the number of underlying components (Nylund et al., 2007).”
• Residual Diagnostics for Growth Mixture Models (2005, background): “Overspecification of K would lead to nonidentifiability of model parameters (Teicher 1963; Jeffries 2003).”