Bootstrap-based statistical inference for linear mixed effects under misspecifications
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Bootstrap-based statistical inference for linear mixed effects under misspecificationsData
2024Cita bibliográfica
Reluga, K., Lombardía, M. J., & Sperlich, S. (2024). Bootstrap-based statistical inference for linear mixed effects under misspecifications. Computational Statistics & Data Analysis, 108014. https://doi.org/10.1016/j.csda.2024.108014
Resumo
[Abstract]: Linear mixed effects are considered excellent predictors of cluster-level parameters in various domains. However, previous research has demonstrated that their performance is affected by departures from model assumptions. Given the common occurrence of these departures in empirical studies, there is a need for inferential methods that are robust to misspecifications while remaining accessible and appealing to practitioners. Statistical tools have been developed for cluster-wise and simultaneous inference for mixed effects under distributional misspecifications, employing a user-friendly semiparametric random effect bootstrap. The merits and limitations of this approach are discussed in the general context of model misspecification. Theoretical analysis demonstrates the asymptotic consistency of the methods under general regularity conditions. Simulations show that the proposed intervals are robust to departures from modelling assumptions, including asymmetry and long tails in the distributions of errors and random effects, outperforming competitors in terms of empirical coverage probability. Finally, the methodology is applied to construct confidence intervals for household income across counties in the Spanish region of Galicia.
Palabras chave
Linear mixed models
Robust bootstrap inference
Small area estimation
Simultaneous inference
Robust bootstrap inference
Small area estimation
Simultaneous inference
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Dereitos
Atribución 4.0 Internacional (CC-BY 4.0)
ISSN
0167-9473 (print)
1872-7352 (electronic)
1872-7352 (electronic)