Model-Based Non-parametric Variance Estimation for Systematic Sampling


Jean D. Opsomer, Department of Statistics, Colorado State University, Fort Collins, CO 80523-1877, USA. E-mail:


Abstract.  Systematic sampling is frequently used in surveys, because of its ease of implementation and its design efficiency. An important drawback of systematic sampling, however, is that no direct estimator of the design variance is available. We describe a new estimator of the model-based expectation of the design variance, under a non-parametric model for the population. The non-parametric model is sufficiently flexible that it can be expected to hold at least approximately in many situations with continuous auxiliary variables observed at the population level. We prove the model consistency of the estimator for both the anticipated variance and the design variance under a non-parametric model with a univariate covariate. The broad applicability of the approach is demonstrated on a dataset from a forestry survey.