Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
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DOI: 10.1007/s41019-019-0091-y
Abstract: This paper proposes a new method called depth difference (DeD), for estimating the optimal number of clusters (k) in a dataset based on data depth. The DeD method estimates the k parameter before actual clustering… read more here.
Keywords: optimal number; depth; number clusters; estimating optimal ... See more keywords
Power comparison of data depth-based nonparametric tests for testing equality of locations
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DOI: 10.1080/00949655.2016.1269329
Abstract: ABSTRACT In the recent years, the notion of data depth has been used in nonparametric multivariate data analysis since it gives natural ‘centre-outward’ ordering of multivariate data points with respect to the given data cloud.… read more here.
Keywords: testing equality; data depth; nonparametric tests; equality locations ... See more keywords
On Evaluation of Ensemble Forecast Calibration Using the Concept of Data Depth
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DOI: 10.1175/mwr-d-16-0351.1
Abstract: AbstractVarious generalizations of the univariate rank histogram have been proposed to inspect the reliability of an ensemble forecast or analysis in multidimensional spaces. Multivariate rank histograms provide insightful information about the misspecification of genuinely multivariate… read more here.
Keywords: data depth; multivariate rank; concept data; ensemble forecast ... See more keywords