A kernel smoothing method of adjusting for unit non‐response in sample surveys

Non‐response is a common problem in survey sampling and this phenomenon can only be ignored at the risk of invalidating inferences from a survey. In order to adjust for unit non‐response, the authors propose a weighting method in which kernel regression is used to estimate the response probabilities. They show that the adjusted estimator is consistent and they derive its asymptotic distribution. They also suggest a means of estimating its variance through a replication‐based technique. Furthermore, a Monte Carlo study allows them to illustrate the properties of the non‐response adjustment and its variance estimator.