Suchergebnisse

Large-scale sample surveys are not designed to produce reliable estimates for small areas. Here, small area estimation methods can be applied to estimate population parameters of target variables to detailed geographic scales. Small area estimation for noncontinuous variables is a topic of great interest in the social sciences where such variables can be found. Generalized linear mixed models are widely adopted in the literature. Interestingly, the small area estimation literature shows that multivariate small area estimators, where correlations among outcome variables are taken into account, produce more efficient estimates than do the traditional univariate techniques. In this article, the author evaluate a multivariate small area estimator on the basis of a joint mixed model in which a small area proportion and mean of a continuous variable are estimated simultaneously. Using this method, the author "borrows strength" across response variables. The author carried out a design-based simulation study to evaluate the approach where the indicators object of study are the income and a monetary poverty (binary) indicator. The author found that the multivariate approach produces more efficient small area estimates than does the univariate modeling approach. The method can be extended to a large variety of indicators on the basis of social surveys.

Abstract
There is growing interest within National Statistical Institutes in combining available datasets containing information on a large variety of social domains. Statistical matching approaches can be used to integrate data sources through a common set of variables where each dataset contains different units that belong to the same target population. However, a common problem is related to the assumption of conditional independence among variables observed in different data sources. In this context, an auxiliary dataset containing all the variables jointly can be used to improve the statistical matching by providing information on the correlation structure of variables observed across different datasets. We propose modifying the prediction models from the auxiliary dataset through a calibration step and show that we can improve the outcome of statistical matching in a variety of settings. We evaluate the proposed approach via simulation and an application based on the European Union Statistics for Income and Living Conditions and Living Costs and Food Survey for the United Kingdom.

Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE approaches is their current inability to deliver reliable measures of uncertainty—and hence confidence intervals—around the small area estimates produced. In this article, we overcome this key limitation via the development of a measure of uncertainty that takes into account both variance and bias, that is, the mean squared error. This new approach is evaluated via a simulation study and demonstrated in a practical application using European Union Statistics on Income and Living Conditions data to explore income levels across Italian municipalities. Evaluations show that the approach proposed delivers accurate estimates of uncertainty and is robust to nonnormal distributions. The approach provides a significant development to widely used spatial microsimulation SAE techniques.

Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE approaches is their current inability to deliver reliable measures of uncertainty - and hence confidence intervals - around the small area estimates produced. In this article, we overcome this key limitation via the development of a measure of uncertainty that takes into account both variance and bias, that is, the mean squared error. This new approach is evaluated via a simulation study and demonstrated in a practical application using European Union Statistics on Income and Living Conditions data to explore income levels across Italian municipalities. Evaluations show that the approach proposed delivers accurate estimates of uncertainty and is robust to nonnormal distributions. The approach provides a significant development to widely used spatial microsimulation SAE techniques.

From SAGE Publishing via Jisc Publications Router ; History: epub 2019-02-13 ; Publication status: Published ; Funder: Economic and Social Research Council; FundRef: https://doi.org/10.13039/501100000269; Grant(s): ES/J500094/1 ; Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.

Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.

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