Small area estimation using a nonparametric model-based direct estimator

Small area estimation using a nonparametric model-based direct estimator

Nonparametric regression is widely used as a method of characterising a non-linear relationship between a variable of interest and a set of covariates. Practical application of nonparametric regression methods in the field of small area estimation is fairly recent, and has so far focussed on the use of empirical best linear unbiased prediction under a model that combines a penalized spline (p-s...

Nonparametric Small Area Estimation Using Penalized Spline Regression

We propose a new small area estimation approach that combines small area random effects with a smooth, nonparametrically specified trend. By using penalized splines as the representation for the nonparametric trend, it is possible to express the small area estimation problem as a mixed effect regression model. We show how this model can be fitted using existing model fitting approaches such as ...

Nonparametric regression methods for small area estimation

Small Area Estimation Using a Spatio-temporal Linear Mixed Model

• In this paper it is proposed a spatio-temporal area level linear mixed model involving spatially correlated and temporally autocorrelated random effects. An empirical best linear unbiased predictor (EBLUP) for small area parameters has been obtained under the proposed model. Using previous research in this area, analytical and bootstrap estimators of the mean squared prediction error (MSPE) o...

A Comparison of model-based methods for Small Area Estimation

Government agencies often provide small area estimates that rely on available data and some underlying model that helps to provide estimates in all areas, even in those that were not sampled. Several models have been well-established for the study of data coming from small areas. In this paper we have made a comparison of some of these methods paying attention to how different types of data set...