Nonparametric regression is widely used as a method of characterizing 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-spline) fit and random area effects. The concept
of model-based direct estimation is used to develop an alternative nonparametric approach
to estimation of a small area mean. The suggested estimator is a weighted average of the
sample values from the area, with weights derived from a linear regression model with
random area effects extended to incorporate a smooth, nonparametrically specified trend.
Estimation of the mean squared error of the proposed small area estimator is also discussed.
Monte Carlo simulations based on both simulated and real datasets show that the
proposed model-based direct estimator and its associated mean squared error estimator
perform well. They are worth considering in small area estimation applications where
the underlying population regression relationships are non-linear or have a complicated