Sampling Designs on Finite Populations with Spreading Control Parameters

TL;DR

This paper introduces new finite population sampling methods using renewal chains and discrete distributions to control the spread of sampled units, enhancing flexibility in sampling designs.

Contribution

It proposes a novel class of sampling designs that allow explicit control over joint inclusion probabilities and the spatial distribution of samples using rank-difference distributions.

Findings

Flexible control over joint inclusion probabilities.

Ability to increase or decrease sample spread.

Validated through simulation studies.

Abstract

We present new sampling methods in finite population that allow to control the joint inclusion probabilities of units and especially the spreading of sampled units in the population. They are based on the use of renewal chains and multivariate discrete distributions to generate the difference of population ranks between two successive selected units. With a Bernoulli sampling design, these differences follow a geometric distribution, and with a simple random sampling design they follow a negative hypergeometric distribution. We propose to use other distributions and introduce a large class of sampling designs with and without fixed sample size. The choice of the rank-difference distribution allows us to control units joint inclusion probabilities with a relatively simple method and closed form formula. Joint inclusion probabilities of neighboring units can be chosen to be larger, or smaller, compared to those of Bernoulli or simple random sampling, thus allowing to more or less spread the sample on the population. This can be useful when neighboring units have similar characteristics or, on the contrary, are very different. A set of simulations illustrates the qualities of this method.