Single Imputation Methods and Confidence Intervals for the Gini Index
Metadatos
Mostrar el registro completo del ítemEditorial
MDPI
Materia
Missing data Variance estimation Coverage Inequality Non-response mechanism
Fecha
2021-12-15Referencia bibliográfica
Álvarez-Verdejo, E.; Moya-Fernández, P.J.; Muñoz-Rosas, J.F Single Imputation Methods and Confidence Intervals for the Gini Index. Mathematics 2021, 9, 3252. [https://doi.org/10.3390/math9243252]
Patrocinador
Ministry of Economy, Industry and Competitiveness Spanish State Research Agency (SRA); European Commission ECO2017-84138-PResumen
The problem of missing data is a common feature in any study, and a single imputation
method is often applied to deal with this problem. The first contribution of this paper is to analyse
the empirical performance of some traditional single imputation methods when they are applied
to the estimation of the Gini index, a popular measure of inequality used in many studies. Various
methods for constructing confidence intervals for the Gini index are also empirically evaluated.
We consider several empirical measures to analyse the performance of estimators and confidence
intervals, allowing us to quantify the magnitude of the non-response bias problem. We find extremely
large biases under certain non-response mechanisms, and this problem gets noticeably worse as
the proportion of missing data increases. For a large correlation coefficient between the target
and auxiliary variables, the regression imputation method may notably mitigate this bias problem,
yielding appropriate mean square errors. We also find that confidence intervals have poor coverage
rates when the probability of data being missing is not uniform, and that the regression imputation
method substantially improves the handling of this problem as the correlation coefficient increases.