REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit-Reference-Cited by-同舟云学术

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REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit

Published:2019-06-16 Issue:11 Volume:50 Page:3397-3419
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Fischer Daniel¹,Berro Alain²,Nordhausen Klaus³,Ruiz-Gazen Anne⁴

Affiliation:

1. Applied Statistical Methods, Natural Resources Institute Finland (Luke), Jokioinen, Finland

2. Institut de Recherche en Informatique de Toulouse, University of Toulouse Capitole, Toulouse, France

3. Institute of Statistics & Mathematical Methods in Economics, Vienna University of Technology, Wien, Austria

4. Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France

Funder

COST Action CRoNoS

COST

Publisher

Informa UK Limited

Subject

Modelling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2019.1626880

Reference30 articles.

1. Berro, A., and S. Larabi Marie-Sainte. 2014. EPP-lab (Version 1.0.1). Zenodo (2014, September 5). doi:10.5281/zenodo.11609.

2. Genetic algorithms and particle swarm optimization for exploratory projection pursuit

3. Exploratory Projection Pursuit

4. Interactive and Dynamic Graphics for Data Analysis

5. A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust embedding and outlier detection of metric space data;SSRN Electronic Journal;2024

2. Large-sample properties of unsupervised estimation of the linear discriminant using projection pursuit;Electronic Journal of Statistics;2021-01-01

3. REPPlabShiny: 'REPPlab' via a Shiny Application;CRAN: Contributed Packages;2020-10-05

4. Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace;Analytical Methods in Statistics;2020

5. ICS for multivariate outlier detection with application to quality control;Computational Statistics & Data Analysis;2018-12

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