Robust density estimation using distance methods

Peter J. Diggle

doi:10.1093/biomet/62.1.39

Robust density estimation using distance methods

Peter J. Diggle

Research output: Contribution to journal › Article › peer-review

52 Scopus citations

Abstract

Distance estimators of density may exhibit serious bias unless the population under consideration forms a completely random spatial pattern, i.e. the estimators are not robust. In this paper some new estimators are proposed, and their robustness is assessed analytically against two stochastic models, which together embrace a continuous range of spatial pattern, from extreme regularity, through randomness, to extreme aggregation.

Original language	English (US)
Pages (from-to)	39-48
Number of pages	10
Journal	Biometrika
Volume	62
Issue number	1
DOIs	https://doi.org/10.1093/biomet/62.1.39
State	Published - Apr 1975
Externally published	Yes

Keywords

Density estimation
Distance method
Ecology
Robustness
Spatial distribution

ASJC Scopus subject areas

Statistics, Probability and Uncertainty
Applied Mathematics
General Mathematics
Statistics and Probability
Agricultural and Biological Sciences (miscellaneous)
General Agricultural and Biological Sciences

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10.1093/biomet/62.1.39

Cite this

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AB - Distance estimators of density may exhibit serious bias unless the population under consideration forms a completely random spatial pattern, i.e. the estimators are not robust. In this paper some new estimators are proposed, and their robustness is assessed analytically against two stochastic models, which together embrace a continuous range of spatial pattern, from extreme regularity, through randomness, to extreme aggregation.

KW - Density estimation

KW - Distance method

KW - Ecology

KW - Robustness

KW - Spatial distribution

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Robust density estimation using distance methods

Abstract

Keywords

ASJC Scopus subject areas

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