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In statistics, statistical depth functions have become increasingly pursued as a useful tool in nonparametric inference for multivariate data.
Statistical depth functions are being used increasingly in nonparametric multivariate data analysis.
Statistical depth functions assign a point its degree of centrality with respect to a dataset.
The present paper advocates the use of multivariate identification and ranking of hotspots based on statistical depth functions, which are useful tools for non-parametric multivariate analysis as they provide center-out ordering of multivariate data.
Our developed tools confirmed these features in each of the images through statistical depth analysis, which led us to believe that the system is well suited to detect possible indications of 3D quality of experience through objective analysis of still images.
There are several statistical depth functions to measure the degree of centrality of a point.
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There was no statistical relationship between depth of last foot stroke and dive order, independently of maximum dive depth (no interaction between dive order and maximum dive depth: F1,130 = 0.34, p = 0.558).
There was also no statistical relationship between depth of last foot stroke and body mass, independently of maximum dive depth (no interaction between body mass and maximum dive depth: F1,130 = 0.32, p = 0.574).
In order of decreasing strength of statistical relationship, soil depth, land use and season were all related to some aspects of soil macrofauna density, biomass and community composition.
Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments.
From these models, transcripts with a strong positive or negative loading were selected for further in depth statistical analysis.
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