Nonparametric Small Area Estimation Using Penalized Spline Regression

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 estimation using penalized least squares

We present multivariate penalized least squares regression estimates. We use Vapnik{ Chervonenkis theory and bounds on the covering numbers to analyze convergence of the estimates. We show strong consistency of the truncated versions of the estimates without any conditions on the underlying distribution.

Nonparametric Small Area Estimation via M-quantile Regression using Penalized Splines

The demand of reliable statistics for small areas, when only reduced sizes of the samples are available, has promoted the development of small area estimation methods. In particular, an approach that is now widely used is based on linear mixed models. Chambers & Tzavidis (2006) have recently proposed an approach for small area estimation that is based on M-quantile models. However, when the fun...

Nonparametric regression methods for small area estimation

Estimation of B-spline Nonparametric Regression Models using Information Criteria

Nonparametric regression modelling has received considerable attention and many methods have been proposed to draw information from data with complex structure. We consider the use of B-spline nonparametric regression models estimated by penalized likelihood methods. A crucial point in constructing the models is in the choice of a smoothing parameter and the number of knots, for which several a...