Uncertainty of a detected spatial cluster in 1D: quantification and visualization

Junho Lee, Ronald E. Gangnon, Jun Zhu, Jingjing Liang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.
Original languageEnglish (US)
Pages (from-to)345-359
Number of pages15
JournalStat
Volume6
Issue number1
DOIs
StatePublished - Oct 19 2017

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