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Uncertainty of a detected spatial cluster in 1D: quantification and visualization
Author(s) -
Lee Junho,
Gang Ronald E.,
Zhu Jun,
Liang Jingjing
Publication year - 2017
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.161
Subject(s) - statistic , cluster (spacecraft) , visualization , set (abstract data type) , scan statistic , computer science , data mining , data set , confidence interval , spatial analysis , statistics , data science , mathematics , artificial intelligence , programming language
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.