A Depth Function and a Scale Curve Based on Spatial Quantiles

By Robert Serfling. 2002

140 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/40f26aa025062843b9c98bb64a2e2ac85b0aed00

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In Statistics and Probability

Citations in the corpus

On Maximum Depth and Related Classifiers (2005, background): “Uniform convergence of the empirical version of spatial depth to its population analogue follows from the work of Koltchinskii (1997) and Serfling (2002), and that proves the theorem.”
On data depth in infinite dimensional spaces (2014, background): “…e.g., Donoho and Gasko 1992), projection depth (PD) (see, e.g., Zuo and Serfling 2000) and spatial depth (SD) (see, e.g., Vardi and Zhang 2000; Serfling 2002), which were originally defined for data in finite dimensional spaces, can have natural extensions into infinite dimensional spaces as…”
Multivariate Functional Halfspace Depth (2014, methodology): “In Slaets et al. (2012) it is shown how the information from the warping procedure can be incorporated into a clustering method for functional data.”
Outlier Detection (2008, background): “Miller and Browning [36] proposed a mixture model for a set of labeled and unlabeled samples.”
Nonparametric Phase-II control charts for monitoring high-dimensional processes with unknown parameters (2020, methodology): “The computational algorithm is available in Vardi and Zhang (2000) and Serfling (2002).”
Location–Scale Depth (2004, background): “(2000), Vardi and Zhang (2000), or Serfling (2002).”
Choosing Among Notions of Multivariate Depth Statistics (2020, background): “(10) • Spatial depth (Serfling, 2002): The spatial depth is defined as DSpa(y”
Nonparametric Multivariate Descriptive Measures Based on Spatial Quantiles (2004, background): “For extension to the multivariate case, one must select a particular version of multivariate quantiles (see Serfling, 2002b, for a partial review of various ad hoc notions).”