Impact of Misspecifications of the Latent Variance–Covariance and Residual Matrices on the Class Enumeration Accuracy of Growth Mixture Models

By Thierno M. O. Diallo, Alexandre J. S. Morin, Huizhong Lu. 2016

219 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d5a8d0b9971bd9926dd6ddce78ffd793bb803a12

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. David Litalien
2. Alexandre J. S. Morin

In Genetics

1. David Litalien
2. Alexandre J. S. Morin

In Statistics and Probability

1. David Litalien
2. Alexandre J. S. Morin
3. Eranda Jayawickreme

Citations in the corpus

• Mixture Modeling for Lifespan Developmental Research (2019, methodology): “An extensive simulation study conducted by Diallo, Morin, and Lu (2016) supported Morin, Maïano et al. (2011) conclusions, leading them to recommend that the LCGA parameterization should be avoided and that, whenever possible, GMA should be estimated while allowing all model parameters to be freely…”
• Fixing the Growth Illusion: New Directions for Research in Resilience and Posttraumatic Growth (2019, background): “Diallo, Morin, and Lu (2016) further confirmed the problems of applying these methodological assumptions by showing that their application led to the overextraction of trajectories; removing these methodological assumptions improved the ability to recover the proper number of trajectories (similar…”