Tukey’s Depth for Object Data

Xiongtao Dai and Sara Lopez-Pintado

Journal of the American Statistical Association, 2023, vol. 118, issue 543, 1760-1772

Abstract: We develop a novel exploratory tool for non-Euclidean object data based on data depth, extending celebrated Tukey’s depth for Euclidean data. The proposed metric halfspace depth, applicable to data objects in a general metric space, assigns to data points depth values that characterize the centrality of these points with respect to the distribution and provides an interpretable center-outward ranking. Desirable theoretical properties that generalize standard depth properties postulated for Euclidean data are established for the metric halfspace depth. The depth median, defined as the deepest point, is shown to have high robustness as a location descriptor both in theory and in simulation. We propose an efficient algorithm to approximate the metric halfspace depth and illustrate its ability to adapt to the intrinsic data geometry. The metric halfspace depth was applied to an Alzheimer’s disease study, revealing group differences in the brain connectivity, modeled as covariance matrices, for subjects in different stages of dementia. Based on phylogenetic trees of seven pathogenic parasites, our proposed metric halfspace depth was also used to construct a meaningful consensus estimate of the evolutionary history and to identify potential outlier trees. Supplementary materials for this article are available online.

Date: 2023
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DOI: 10.1080/01621459.2021.2011298

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