Quasi-systematic sampling from a continuous population

Matthieu Wilhelm, Yves Tillé and Lionel Qualité

Computational Statistics & Data Analysis, 2017, vol. 105, issue C, 11-23

Abstract: A specific family of point processes is introduced that allow to select samples for the purpose of estimating the mean or the integral of a function of a real variable. These processes, called quasi-systematic processes, depend on a tuning parameter r>0 that permits to control the likeliness of jointly selecting neighbor units in a same sample. When r is large, units that are close tend to not be selected together and samples are well spread. When r tends to infinity, the sampling design is close to systematic sampling. For all r>0, the first and second-order unit inclusion densities are positive, allowing for unbiased estimators of variance. Algorithms to generate these sampling processes for any positive real value of r are presented. When r is large, the estimator of variance is unstable. It follows that r must be chosen by the practitioner as a trade-off between an accurate estimation of the target parameter and an accurate estimation of the variance of the parameter estimator. The method’s advantages are illustrated with a set of simulations.

Keywords: Binomial process; Point process; Poisson process; Renewal process; Systematic sampling (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:105:y:2017:i:c:p:11-23

DOI: 10.1016/j.csda.2016.07.011

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