The GLD-plot: A depth-based plot to investigate unimodality of directional data

04/24/2021
by   Giuseppe Pandolfo, et al.
0

A graphical tool for investigating unimodality of hyperspherical data is proposed. It is based on the notion of statistical data depth function for directional data which extends the univariate concept of rank. Firstly a local version of distance-based depths for directional data based on aims at analyzing the local structure of hyperspherical data is proposed. Then such notion is compared to the global version of data depth by means of a two-dimensional scatterplot, i.e. the GLD-plot. The proposal is illustrated on simulated and real data examples.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

03/09/2018

A local depth measure for general data

We herein introduce a general local depth measure for data in a Banach s...
10/23/2020

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data

Directional data consist of observations distributed on a (hyper)sphere,...
07/20/2012

Fast nonparametric classification based on data depth

A new procedure, called DDa-procedure, is developed to solve the problem...
07/29/2021

Statistical depth in abstract metric spaces

The concept of depth has proved very important for multivariate and func...
04/09/2017

An Outlyingness Matrix for Multivariate Functional Data Classification

The classification of multivariate functional data is an important task ...
01/14/2022

Eikonal depth: an optimal control approach to statistical depths

Statistical depths provide a fundamental generalization of quantiles and...
01/01/2019

Depth for curve data and applications

John W. Tukey (1975) defined statistical data depth as a function that d...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.