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Bootstrap-based statistical inference for linear mixed effects under misspecifications
dc.contributor.author | Reluga, Katarzyna | |
dc.contributor.author | Lombardía, María José | |
dc.contributor.author | Sperlich, Stefan | |
dc.date.accessioned | 2024-07-15T16:40:59Z | |
dc.date.available | 2024-07-15T16:40:59Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | 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 | es_ES |
dc.identifier.issn | 0167-9473 (print) | |
dc.identifier.issn | 1872-7352 (electronic) | |
dc.identifier.uri | http://hdl.handle.net/2183/38026 | |
dc.description.abstract | [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. | es_ES |
dc.description.sponsorship | The authors gratefully acknowledge support from the Swiss National Science Foundation, projects 200021-192345 and P2GEP2-195898, as well as from the Instituto Galego de Estatística who provided us with the data set. In addition, this research has been supported by MICINN grant PID2020-113578RB-I00, and by Xunta de Galicia (Grupos de Referencia Competitiva ED431C 2020/14), GAIN (Galician Innovation Agency) and the Regional Ministry of Economy, Employment and Industry grant COV20/00604 and Centro de investigación del Sistema universitario de Galicia ED431G 2019/01, all of them through ERDF. The computations were performed at the University of Geneva using Baobab and Yggdrasil HPC Service and using the computational facilities of the Advanced Computing Research Centre, University of Bristol. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Switzerland. Swiss National Science Foundation; 200021-192345 | es_ES |
dc.description.sponsorship | Switzerland. Swiss National Science Foundation; P2GEP2-195898 | es_ES |
dc.description.sponsorship | Xunta de Galicia; COV20/00604 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.csda.2024.108014 | es_ES |
dc.rights | Atribución 4.0 Internacional (CC-BY 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Linear mixed models | es_ES |
dc.subject | Robust bootstrap inference | es_ES |
dc.subject | Small area estimation | es_ES |
dc.subject | Simultaneous inference | es_ES |
dc.title | Bootstrap-based statistical inference for linear mixed effects under misspecifications | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Computational Statistics & Data Analysis | es_ES |
UDC.volume | 199 | es_ES |
UDC.issue | 108014 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 13 | es_ES |
dc.identifier.doi | 10.1016/j.csda.2024.108014 |
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