TY - JOUR
T1 - The t linear mixed model
T2 - model formulation, identifiability and estimation
AU - Regis, Marta
AU - Brini, Alberto
AU - Nooraee, N.
AU - Haakma, Reinder
AU - van den Heuvel, Edwin R.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Heterogeneous variances
KW - Latent variable
KW - Model identifiability
KW - Outliers
KW - Variance components
UR - http://www.scopus.com/inward/record.url?scp=85077356054&partnerID=8YFLogxK
U2 - 10.1080/03610918.2019.1694153
DO - 10.1080/03610918.2019.1694153
M3 - Article
AN - SCOPUS:85077356054
VL - 51
SP - 2318
EP - 2342
JO - Communications in Statistics. Part B, Simulation and Computation
JF - Communications in Statistics. Part B, Simulation and Computation
SN - 0361-0918
IS - 5
ER -