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Research article - Peer-reviewed, 2021

Combining Environmental Area Frame Surveys of a Finite Population

Prentius, Wilmer; Zhao, Xin; Grafstrom, Anton

Abstract

New ways to combine data from multiple environmental area frame surveys of a finite population are being introduced. Environmental surveys often sample finite populations through area frames. However, to combine multiple surveys without risking bias, design components (inclusion probabilities, etc.) are needed at unit level of the finite population. We show how to derive the design components and exemplify this for three commonly used area frame sampling designs. We show how to produce an unbiased estimator using data from multiple surveys, and how to reduce the risk of introducing significant bias in linear combinations of estimators from multiple surveys. If separate estimators and variance estimators are used in linear combinations, there's a risk of introducing negative bias. By using pooled variance estimators, the bias of a linear combination estimator can be reduced. National environmental surveys often provide good estimators at national level, while being too sparse to provide sufficiently good estimators for some domains. With the proposed methods, one can plan extra sampling efforts for such domains, without discarding readily available information from the aggregate/national survey. Through simulation, we show that the proposed methods are either unbiased, or yield low variance with small bias, compared to traditionally used methods.

Keywords

Combining data sources; Combining estimators; Environmental monitoring; Linear combination estimator; Sample design properties

Published in

Journal of Agricultural, Biological, and Environmental Statistics
2021, volume: 26, number: 2, pages: 250-266
Publisher: SPRINGER

Authors' information

Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Zhao, Xin
Swedish University of Agricultural Sciences, Department of Forest Resource Management

UKÄ Subject classification

Environmental Sciences

Publication Identifiers

DOI: https://doi.org/10.1007/s13253-020-00425-z

URI (permanent link to this page)

https://res.slu.se/id/publ/110373