Jean Opsomer

Nonparametric Regression in Survey Estimation

Abstract:

Nonparametric regression methods have traditionally been mostly
ignored by survey statisticians.  We describe two survey estimation
areas in which nonparametric techniques can readily be incorporated
and improve the precision of estimators.   Penalized splines
regression, a new and easy-to-use nonparametric method, is used in
both cases.  In the first area, we will show how model-assisted
estimation can be extended from the commonly used linear and ratio
models to incorporate nonparametric and semiparametric models.  The
applicability of the estimator is illustrated using an example from a
forest health monitoring survey in Utah (USA).  For the second area,
we propose a new approach for performing small area estimation, in
which the mean function is nonparametrically specified.  This is
illustrated using data from a ecological health survey of lakes in the
Northeastern states of the USA.