Comment on Article by Ferreira and Gamerman

TL;DR

This paper discusses a utility-function approach to optimal spatial sampling design, emphasizing the importance of capturing various utility components and incorporating randomness for flexible analysis.

Contribution

It highlights the significance of including all utility aspects and randomness in spatial sampling design, advocating for a balanced approach between model-based and design-based analysis.

Findings

Utility-function approach effectively quantifies optimality

Inclusion of scientific impact and sampling cost improves design

Designed randomness enables non-parametric analysis

Abstract

A utility-function approach to optimal spatial sampling design is a powerful way to quantify what "optimality" means. The emphasis then should be to capture all possible contributions to utility, including scientific impact and the cost of sampling. The resulting sampling plan should contain a component of designed randomness that would allow for a non-parametric design-based analysis if model-based assumptions were in doubt. [arXiv:1509.03410]