Calibrated Weighting for Small Area Estimation

Calibrated weighting methods for estimation of survey population characteristics are widely used. At the same time, model-based prediction methods for estimation of small area or domain characteristics are becoming increasingly popular. This paper explores weighting methods based on the mixed models that underpin small area estimates to see whether they can deliver equivalent small area estimation performance when compared with standard prediction methods and superior population level estimation performance when compared with standard calibrated weighting methods. A simple MSE estimator for weighted small area estimation is also developed

New important developments in small area estimation

The purpose of this paper is to review and discuss some of the new important developments in small area estimation (SAE) methods. Rao (2003) wrote a very comprehensive book, which covers all the main developments in this topic until that time and so the focus of this review is on new developments in the last 7 years. However, to make the review more self contained, I also repeat shortly some of the older developments. The review covers both design based and model-dependent methods with emphasis on the prediction of the area target quantities and the assessment of the prediction error. The style of the paper is similar to the style of my previous review on SAE published in 2002, explaining the new problems investigated and describing the proposed solutions, but without dwelling on theoretical details, which can be found in the original articles. I am hoping that this paper will be useful both to researchers who like to learn more on the research carried out in SAE and to practitioners who might be interested in the application of the new methods

Small-area estimation with spatial similarity

A class of composite estimators of small area quantities that exploit spatial (distancerelated) similarity is derived. It is based on a distribution-free model for the areas, but the estimators are aimed to have optimal design-based properties. Composition is applied also to estimate some of the global parameters on which the small area estimators depend. It is shown that the commonly adopted assumption of random effects is not necessary for exploiting the similarity of the districts (borrowing strength across the districts). The methods are applied in the estimation of the mean household sizes and the proportions of single-member households in the counties (comarcas) of Catalonia. The simplest version of the estimators is more efficient than the established alternatives, even though the extent of spatial similarity is quite modest.Auxiliary information, composite estimation, design-based estimator, exploiting similarity, model-based estimator, multivariate shrinkage, small-area estimation, spatial similarity

Small area estimation on poverty indicators

We propose to estimate non-linear small area population quantities by using Empirical Best (EB) estimators based on a nested error model. EB estimators are obtained by Monte Carlo approximation. We focus on poverty indicators as particular non-linear quantities of interest, but the proposed methodology is applicable to general non-linear quantities. Small sample properties of EB estimators are analyzed by model-based and design-based simulation studies. Results show large reductions in mean squared error relative to direct estimators and estimators obtained by simulated censuses. An application is also given to estimate poverty incidences and poverty gaps in Spanish provinces by sex with mean squared errors estimated by parametric bootstrap. In the Spanish data, results show a significant reduction in coefficient of variation of the proposed EB estimators over direct estimators for most domains.Empirical best estimator, Parametric bootstrap, Poverty mapping, Small area estimation

DETERMINING POVERTY MAP USING SMALL AREA ESTIMATION METHOD

Poverty is a phenomenon that always occurs in every country especially in the developing country such as Indonesia. Poverty is defined as a condition where someone has not capability to fulfill their basic needs (food and non food). The difference of geographic condition and the unequal of demography always become some problems in the geographic targeting of the poor in the poverty reduction program. One of method that is accurately effective and sensitive with poverty in the small area is Small Area Estimation method by Elbers et al. It is known as Elbers, Lanjouw, Lanjouw method (ELL method). The objective of this method is to map the incidence of poverty in every county or city using the steps in ELL method. In this study, we use Central Java as our case study. The results of this study are the model consumption of Central Java, poverty indicators for each city in Central Java and the poverty maps so that can give information and facilitate the government for making priority in poverty reduction programs. Keywords: Poverty map, ELL Metho

Robust small area estimation

Small area estimation has long been a popular and important research topic in survey statistics. For the basic area level model, popularly known as Fay-Herriot model, we first make inference without any distributional assumptions with the exception of a few moment assumptions. In the process, we propose a new method of model parameter estimation, study its statistical properties and use the resulting parameter estimators as components in small area estimators. The second order approximation of the mean squared error of the proposed small area estimators is derived, and we also describe a second order correct estimator of the mean squared error. Then we develop confidence intervals for the small area parameters that are second order correct under normal distributional assumptions. For the unit level model, popularly known as nested-error regression model, we introduce a model-based design consistent estimator for a finite population domain mean

Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation

In this paper, some vital methodological issues of spatial microsimulation modelling for small area estimation have been addressed, with a particular emphasis given to the reweighting techniques. Most of the review articles in small area estimation have highlighted methodologies based on various statistical models and theories. However, spatial microsimulation modelling is emerging as a very useful alternative means of small area estimation. Our findings demonstrate that spatial microsimulation models are robust and have advantages over other type of models used for small area estimation. The technique uses different methodologies typically based on geographic models and various economic theories. In contrast to statistical model-based approaches, the spatial microsimulation model-based approaches can operate through reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different iterative algorithms and that their properties also vary. The study also points out a new method for spatial microsimulation modellingBayesian prediction approach; combinatorial optimisation; GREGWT; microdata; small area estimation; spatial microsimulation