A pseudo‐empirical best linear unbiased prediction approach to small area estimation using survey weights

You, Yong; Rao, J. N. K.

doi:10.2307/3316146

Cited by 87 publications

(118 citation statements)

References 6 publications

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“…For the special case of a domain mean H i =Ȳ i , if β is estimated by the weighted regression estimatorβ w given in You and Rao (2002), the census PEB estimator of H i =Ȳ i equals the pseudo EBLUP of You and Rao (2002). Similarly, the PEB estimator obtained from (12) tends to the pseudo EBLUP as the domain sampling fraction f i = n i /N i becomes small.…”

Section: Pseudo Eb Methodsmentioning

confidence: 99%

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Small area estimation of general parameters under complex sampling designs

Guadarrama

Molina

Rao

2018

Computational Statistics & Data Analysis

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When the probabilities of selecting the individuals for the sample depend on the outcome values, we say that the selection mechanism is informative. Under informative selection, individuals with certain outcome values appear more often in the sample and therefore the sample is not representative of the population. As a consequence, usual model-based inference based on the actual sample without appropriate weighting might be strongly biased. For estimation of general non-linear parameters in small areas, we propose a model-based pseudo empirical best (PEB) method that incorporates the sampling weights and reduces considerably the bias of the unweighted empirical best (EB) estimators under informative selection mechanisms. We analyze the properties of this new method in simulation experiments carried out under complex sampling designs, including informative selection. Our results confirm that the proposed weighted PEB estimators perform significantly better than the unweighted EB estimators in terms of bias under informative sampling, and compare favorably under non-informative sampling. In an application to poverty mapping in Spain, we compare the proposed weighted PEB estimators with the unweighted EB analogues.Keywords: Empirical best estimator; Nested-error model; Poverty mapping; Pseudo empirical best estimator; Unit level models. Abstract: When the probabilities of selecting the individuals for the sample depend on the outcome values, we say that the selection mechanism is informative. Under informative selection, individuals with certain outcome values appear more often in the sample and therefore the sample is not representative of the population. As a consequence, usual model-based inference based on the actual sample without appropriate weighting might be strongly biased. For estimation of general non-linear parameters in small areas, we propose a model-based pseudo empirical best (PEB) method that incorporates the sampling weights and reduces considerably the bias of the unweighted empirical best (EB) estimators under informative selection mechanisms. We analyze the properties of this new method in simulation experiments carried out under complex sampling designs, including informative selection. Our results confirm that the proposed weighted PEB estimators perform significantly better than the unweighted EB estimators in terms of bias under informative sampling, and compare favorably under non-informative sampling. In an application to poverty mapping in Spain, we compare the proposed weighted PEB estimators with the unweighted EB analogues.

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Section: Pseudo Eb Methodsmentioning

confidence: 99%

“…We define the pseudo EB (PEB) predictor as the PB predictor with θ replaced by a consistent estimator such as maximum likelihood (ML), restricted ML (REML) estimators or estimators based on the method of moments (You and Rao, 2002).…”

Section: Pseudo Eb Methodsmentioning

confidence: 99%

Small area estimation of general parameters under complex sampling designs

Guadarrama

Molina

Rao

2018

Computational Statistics & Data Analysis

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“…Note that we could alternatively estimate b and k using design weights. You and Rao (2002) propose to transform the unit-level model (7) to a survey-weighted domain-level model with normalized weights within the domains. This model is given by…”

Section: Small Area Estimators Under Complex Designsmentioning

confidence: 99%

The Impact of Sampling Designs on Small Area Estimates for Business Data

Burgard

Münnich

Zimmermann

2014

Journal of Official Statistics

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Evidence-based policy making and economic decision making rely on accurate business information on a national level and increasingly also on smaller regions and business classes. In general, traditional design-based methods suffer from low accuracy in the case of very small sample sizes in certain subgroups, whereas model-based methods, such as small area techniques, heavily rely on strong statistical models.In small area applications in business statistics, two major issues may occur. First, in many countries business registers do not deliver strong auxiliary information for adequate model building. Second, sampling designs in business surveys are generally nonignorable and contain a large variation of survey weights.The present study focuses on the performance of small area point and accuracy estimates of business statistics under different sampling designs. Different strategies of including sampling design information in the models are discussed. A design-based Monte Carlo simulation study unveils the impact of the variability of design weights and different levels of aggregation on model-versus design-based estimation methods. This study is based on a close to reality data set generated from Italian business data.

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“…Some of these techniques are model-dependent, as their estimates have good statistical properties but only if the model is correct (they are not robust). In order to reduce the risks of an incorrect specification of the model, mixed appraisal techniques have been suggested, providing indirect estimates based on models and consistent according to design (robust) (You and Rao, 2002;Jiang and Lahiri, 2006b). …”

Section: Estimation In Small Areasmentioning

confidence: 99%

Agricultural and environmental information systems: the integrating role of area samples

Ambrósio

Marín

Iglesias

et al. 2009

Span. j. agric. res.

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This article aims to be a contribution to the improvement of agricultural and environmental official statistics. Methods are applied to integrate information from state agency registers regarding crop area with ground data observed in random area samples. To improve the precision of crop area estimates in small areas (municipalities), methods using ground survey and remote sensing are applied. To improve temporal resolution of crop area estimators, methods based on time series analysis are applied. Agro-meteorological models are applied to improve crop yields statistics. A method is shown whereby crop rotation models may be a useful tool to forecast changes in the dynamics of the use of natural resources (soil, water and air) by agriculture and to foresee their environmental impact. Finally, a method to update and disaggregate information from territorial censuses on land uses is applied. These methods and models are illustrated in the framework of an information system belonging to the Spanish Ministry of the Environment and Rural and Marine Affairs. The relative improvement offered by each method is assessed by evaluating the precision gain of the proposed crop area estimates versus those currently used by the aforementioned information system. Additional key words: agrometeorological models, crop rotation models, sampling in time, small area estimation, spatiotemporal econometric models, updating regional censuses and disaggregation of information. Resumen Sistemas de información agraria y medioambiental: el papel integrador de las muestras de áreasEste artículo pretende ser una contribución a la mejora de las estadísticas oficiales agrarias y medioambientales. Se aplican métodos para mejorar la precisión de las estimaciones anuales de la superficie de cultivos, integrando la información relativa a la superficie de cultivos registrada en los registros administrativos con los datos de campo observados en una muestra aleatoria de áreas. Para mejorar la precisión de las estimaciones de la superficie de cultivos en áreas pequeñas (municipios), se aplican métodos que integran datos de teledetección con datos de campo observados en muestras de áreas. Para mejorar la resolución temporal de los estimadores de la superficie de cultivos se aplican métodos de análisis de series temporales a los datos observados en muestras de áreas. Para mejorar las estadísti-cas sobre el rendimiento de las cosechas se aplican modelos agrometeorológicos integrados en muestras de áreas. Se muestra cómo los modelos de rotación de cultivos basados en muestras de áreas pueden ser una herramienta útil para anticiparse a cambios en la dinámica del uso de los recursos naturales (suelo, agua y aire) por parte de la agricultura y prever su impacto medioambiental. Finalmente, se aplica un método para actualizar y desagregar la información de censos territoriales sobre los usos del suelo, a partir de modelos de rotación de cultivos y de muestras de áreas. Estos métodos y modelos se ilustran en el marco de un sistema de información pertenec...

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A pseudo‐empirical best linear unbiased prediction approach to small area estimation using survey weights

Cited by 87 publications

References 6 publications

Small area estimation of general parameters under complex sampling designs

Small area estimation of general parameters under complex sampling designs

The Impact of Sampling Designs on Small Area Estimates for Business Data

Agricultural and environmental information systems: the integrating role of area samples

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