Some Empirical Results on Nearest-Neighbour Pseudo-populations for Resampling from Spatial Populations

Sara Franceschi, Rosa Maria Di Biase, Agnese Marcelli and Lorenzo Fattorini
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Sara Franceschi: Department of Economic and Statistics, University of Siena, 53100 Siena, Italy
Rosa Maria Di Biase: Department of Sociology and Social Research, University of Milano Bicocca, 20126 Milan, Italy
Agnese Marcelli: Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, 01100 Viterbo, Italy
Lorenzo Fattorini: Department of Economic and Statistics, University of Siena, 53100 Siena, Italy

Stats, 2022, vol. 5, issue 2, 1-16

Abstract: In finite populations, pseudo-population bootstrap is the sole method preserving the spirit of the original bootstrap performed from iid observations. In spatial sampling, theoretical results about the convergence of bootstrap distributions to the actual distributions of estimators are lacking, owing to the failure of spatially balanced sampling designs to converge to the maximum entropy design. In addition, the issue of creating pseudo-populations able to mimic the characteristics of real populations is challenging in spatial frameworks where spatial trends, relationships, and similarities among neighbouring locations are invariably present. In this paper, we propose the use of the nearest-neighbour interpolation of spatial populations for constructing pseudo-populations that converge to real populations under mild conditions. The effectiveness of these proposals with respect to traditional pseudo-populations is empirically checked by a simulation study.

Keywords: spatial surveys; Horvitz–Thompson estimator; spatially balance sampling; nonmeasurable designs; pseudo-population bootstrap; nearest-neighbour criterion; simulation study (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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