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Title: The Impact of Covariance Misspecification in Multivariate Gaussian Mixtures on Estimation and Inference: An Application to Longitudinal Modeling
Resulting in 1 citation.
1. Heggeseth, Brianna
Jewell, Nicholas P.
The Impact of Covariance Misspecification in Multivariate Gaussian Mixtures on Estimation and Inference: An Application to Longitudinal Modeling
Statistics in Medicine 32,16 (20 July 2013): 2790-2803.
Also: http://onlinelibrary.wiley.com/doi/10.1002/sim.5729/abstract
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Body Mass Index (BMI); Modeling; Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence—that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.
Bibliography Citation
Heggeseth, Brianna and Nicholas P. Jewell. "The Impact of Covariance Misspecification in Multivariate Gaussian Mixtures on Estimation and Inference: An Application to Longitudinal Modeling." Statistics in Medicine 32,16 (20 July 2013): 2790-2803.