Kernel-Type Smoothing Methods of Adjusting for Unit Nonresponse in Presence of Multiple and Different Type Covariates

This paper deals with the nonresponse problem in the estimation of the mean of a finite population following a nonparametric approach. Weighting adjustment is a popular method for handling unit nonresponse. It operates by increasing the sampling weights of the respondents in the sample using estimates of their respond probabilities. Typically, these estimates are obtained by fitting parametric models relating response occurrences and auxiliary variables. An alternative solution is the nonparametric estimation of the response probabilities. The aim of this paper is to investigate, via simulation experiments, the small-sample properties of kernel regression estimation of the response probabilities when the auxiliary information consists in a mix of continuous and discrete variables. Furthermore the practical behavior of the method is evaluated on data of a web survey on accommodation facilities in the province of Florence.