An Optimal Algorithm for Computing the Spherical Depth of Points in the Plane
David Bremner, Rasoul Shahsavarifar

TL;DR
This paper introduces an optimal $O(n \,\log n)$ algorithm for computing the spherical depth of points in the plane, significantly improving over the naive $O(dn^2)$ approach, and explores its geometric properties and relationships with simplicial depth.
Contribution
The paper presents the first optimal algorithm for bivariate spherical depth computation and establishes a lower bound, advancing the efficiency of multivariate data analysis methods.
Findings
The algorithm runs in $O(n \log n)$ time for the plane.
Computing spherical depth has a proven lower bound of $\Omega(n \log n)$ time.
Spherical depth is linearly bounded by simplicial depth, with experimental evidence suggesting a stronger bound.
Abstract
For a distribution function on and a point , the \emph{spherical depth} is defined to be the probability that a point is contained inside a random closed hyper-ball obtained from a pair of points from . The spherical depth is also defined for an arbitrary data set and . This definition is based on counting all of the closed hyper-balls, obtained from pairs of points in , that contain . The significant advantage of using the spherical depth in multivariate data analysis is related to its complexity of computation. Unlike most other data depths, the time complexity of the spherical depth grows linearly rather than exponentially in the dimension . The straightforward algorithm for computing the spherical depth in dimension takes . The main result of this…
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Taxonomy
TopicsStatistical and numerical algorithms · Advanced Statistical Methods and Models · Soil Geostatistics and Mapping
