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Mostrando ítems 11-16 de 16
Simultaneous Inference for Empirical Best Predictors With a Poverty Study in Small Areas
(Taylor and Francis Group, 2023-01)
[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 ...
A New Approach to the Gender Pay Gap Decomposition by Economic Activity
(Oxford University Press, 2022-01)
[Abstract]: The aim of this paper is to present an original approach to estimate the gender pay gap (GPG). We propose a model-based decomposition, similar to the most popular approaches, where the first component measures ...
Poverty Mapping Under Area-Level Random Regression Coefficient Poisson Models
(Oxford University Press, 2023-11)
[Abstract]: Under an area-level random regression coefficient Poisson model, this article derives small area predictors of counts and proportions and introduces bootstrap estimators of the mean squared errors (MSEs). The ...
Small area estimation of proportions under area-level compositional mixed models
(Springer Nature, 2020-09)
[Abstract]: This paper introduces area-level compositional mixed models by applying transformations to a multivariate Fay–Herriot model. Small area estimators of the proportions of the categories of a classification variable ...
Selection model for domains across time: application to labour force survey by economic activities
(Springer Nature, 2021-03)
[Abstract]: This paper introduces a small area estimation approach that borrows strength across domains (areas) and time and is efficiently used to obtain labour force estimators by economic activity. Specifically, the ...
Bootstrap-based statistical inference for linear mixed effects under misspecifications
(Elsevier, 2024)
[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 ...