Simultaneous Inference for Empirical Best Predictors With a Poverty Study in Small Areas
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Simultaneous Inference for Empirical Best Predictors With a Poverty Study in Small AreasDate
2023-01Citation
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.
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.
Keywords
EBP
Generalized mixed models
Mixed parameters
Small area estimation
Uniform inference
Generalized mixed models
Mixed parameters
Small area estimation
Uniform inference
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1537-274X