Uncertainty of a detected spatial cluster in 1D: Quantification and visualization
Document Type
Article
Publication Date
1-1-2017
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.
Publication Source (Journal or Book title)
Stat
First Page
345
Last Page
359
Recommended Citation
Lee, J., Gangnon, R., Zhu, J., & Liang, J. (2017). Uncertainty of a detected spatial cluster in 1D: Quantification and visualization. Stat, 6 (1), 345-359. https://doi.org/10.1002/sta4.161