Why Well Spread Probability Samples Are Balanced

By Anton Grafström, Niklas L. P. Lundström. 2013

80 citations recorded by the source corpus.

When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the HorvitzThompson estimator, is a good strategy in a varsity of situations.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b5fbc708a4dafc5e27b8b699abaf538861f97edb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lina Schelin
2. Anton Grafström

In Statistics and Probability

1. Lina Schelin
2. Anton Grafström
3. Niklas L. P. Lundström

Citations in the corpus

• How to Select Representative Samples (2014, methodology): “A new method that allows to select samples that are both well spread and balanced was recently introduced by Grafström & Tillé (2013).”