Mostrar o rexistro simple do ítem
Simultaneous Inference for Empirical Best Predictors With a Poverty Study in Small Areas
dc.contributor.author | Reluga, Katarzyna | |
dc.contributor.author | Lombardía, María José | |
dc.contributor.author | Sperlich, Stefan | |
dc.date.accessioned | 2023-12-12T13:57:19Z | |
dc.date.available | 2023-12-12T13:57:19Z | |
dc.date.issued | 2023-01 | |
dc.identifier.citation | K. Reluga, M.-J. Lombardía, y S. Sperlich, «Simultaneous Inference for Empirical Best Predictors With a Poverty Study in Small Areas», Journal of the American Statistical Association, vol. 118, n.º 541, pp. 583-595, ene. 2023, doi: 10.1080/01621459.2021.1942014. | es_ES |
dc.identifier.issn | 1537-274X | |
dc.identifier.uri | http://hdl.handle.net/2183/34462 | |
dc.description.abstract | [Abstract]: Today, generalized linear mixed models (GLMM) are broadly used in many fields. However, the development of tools for performing simultaneous inference has been largely neglected in this domain. A framework for joint inference is indispensable to carry out statistically valid multiple comparisons of parameters of interest between all or several clusters. We therefore develop simultaneous confidence intervals and multiple testing procedures for empirical best predictors under GLMM. In addition, we implement our methodology to study widely employed examples of mixed models, that is, the unit-level binomial, the area-level Poisson-gamma and the area-level Poisson-lognormal mixed models. The asymptotic results are accompanied by extensive simulations. A case study on predicting poverty rates illustrates applicability and advantages of our simultaneous inference tools. | es_ES |
dc.description.sponsorship | The authors gratefully acknowledge the support from the Swiss National Science Foundation for the project 200021-192345. In addition, they acknowledge the support from the MINECO grants MTM2017-82724-R and MTM2014-52876-R, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015 and Centro Singular de Investigación de Galicia ED431G/01), all of them through the ERDF. The computations were performed at the University of Geneva on the Baobab cluster. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2016-015 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Taylor and Francis Group | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Programa Estatal de I+D+I Orientada a los Retos de la Sociedad/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRIALES | es_ES |
dc.relation.uri | https://doi.org/10.1080/01621459.2021.1942014 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | EBP | es_ES |
dc.subject | Generalized mixed models | es_ES |
dc.subject | Mixed parameters | es_ES |
dc.subject | Small area estimation | es_ES |
dc.subject | Uniform inference | es_ES |
dc.title | Simultaneous Inference for Empirical Best Predictors With a Poverty Study in Small Areas | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Journal of the American Statistical Association | es_ES |
UDC.volume | 118 | es_ES |
UDC.issue | 541 | es_ES |
Ficheiros no ítem
Este ítem aparece na(s) seguinte(s) colección(s)
-
GI-MODES - Artigos [139]