Nonparametric methods in survey sampling

Abstract

Nonparametric techniques have only recently been employed in the estimation procedure of finite population parameters in a model-assisted framework. When complete auxiliary information is available, the use of more flexible methods to predict the value taken by the survey variable in non sampled units allows building more efficient estimators. Here we consider a general class of nonparametric regression estimators of a finite population mean. Four different nonparametric techniques that can handle multivariate auxiliary information are employed, their properties stated and their performance compared by means of a simulation study.