Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 51, Issue 5, Pages 2318-2342Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2019.1694153
Keywords
Heterogeneous variances; Latent variable; Model identifiability; Outliers; Variance components
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Funding
- Philips Electronics Nederland B.V
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This paper provides an in-depth analysis of the t linear mixed model (tLMM), evaluating a direct maximum likelihood estimation method through extensive simulations and investigating identifiability properties.
The robustness of the t linear mixed model (tLMM) has been proved and exploited in many applications. Various publications emerged with the aim of proving superiority with respect to traditional linear mixed models, extending to more general settings and proposing more efficient estimation methods. However, little attention has been paid to the mathematical properties of the model itself and to the evaluation of the proposed estimation methods. In this paper we perform an in-depth analysis of the tLMM, evaluating a direct maximum likelihood estimation method via an intensive simulation study and investigating some identifiability properties. The theoretical findings are illustrated through an application to a dataset collected from a sleep trial.
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