Identifying density-based local outliers in medical multivariate circular data
- 20 September 2020
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 39 (21), 2793-2798
- https://doi.org/10.1002/sim.8576
Abstract
This article is considered to be the first to deal with the problem of outlier-detection in multivariate circular data. The proposed algorithm is an extension of the Local Outlier Factor (LOF) method. Two different circular distances are used; taking into account the close bounded range of circular variables, and testing all possible permutations. The performance of the algorithm is investigated via an extensive simulation study. The performance of the LOF algorithm has a direct relationship with concentration parameter, while it has an inverse relationship with the sample size. For illustrative purposes, the algorithm has been implemented on two medical multivariate circular data, namely, X-ray beam projectors data and eye data. The extension of the LOF algorithm for other types of directional data such as spherical and cylindrical datasets is worth to be investigated.Keywords
This publication has 17 references indexed in Scilit:
- Regression analysis of correlated circular data based on the multivariate von Mises distributionEnvironmental and Ecological Statistics, 2015
- A New Discordancy Test in Circular Data Using Spacings TheoryCommunications in Statistics - Simulation and Computation, 2015
- Unsupervised clustering of multivariate circular dataStatistics in Medicine, 2012
- Model-based clustering of multivariate skew data with circular components and missing valuesJournal of Applied Statistics, 2012
- Boxplot for circular variablesComputational Statistics, 2011
- Estimation of Parameters Subject to Order Restrictions on a Circle With Application to Estimation of Phase Angles of Cell Cycle GenesJournal of the American Statistical Association, 2009
- Protein Bioinformatics and Mixtures of Bivariate von Mises Distributions for Angular DataBiometrics, 2006
- Directional features in online handwriting recognitionPattern Recognition, 2005
- Detection of Outliers in Multivariate Data: A Method Based on Clustering and Robust EstimatorsPublished by Springer Science and Business Media LLC ,2002
- Outliers in Circular DataJournal of the Royal Statistical Society Series C: Applied Statistics, 1980