A Look into the Problem of Preferential Sampling from the Lens of Survey Statistics

TL;DR

This paper explores preferential sampling in spatial and ecological statistics, comparing it with survey sampling, reviewing solutions, and conducting simulations and data analysis to understand biases and methods for correction.

Contribution

It provides a comparative analysis of preferential sampling and survey sampling, highlighting differences, and evaluates various methods through simulations and real data analysis.

Findings

Different modeling techniques are needed for preferential sampling and survey sampling.

Simulation studies reveal strengths and limitations of existing methods.

Heavy metal biomonitoring data analysis illustrates practical implications.

Abstract

An evolving problem in the field of spatial and ecological statistics is that of preferential sampling, where biases may be present due to a relationship between sample data locations and a response of interest. This field of research bears a striking resemblance to the longstanding problem of informative sampling within survey methodology, although with some important distinctions. With the goal of promoting collaborative effort within and between these two problem domains, we make comparisons and contrasts between the two problem statements. Specifically, we review many of the solutions available to address each of these problems, noting the important differences in modeling techniques. Additionally, we construct a series of simulation studies to examine some of the methods available for preferential sampling, as well as a comparison analyzing heavy metal biomonitoring data.