Multivariate outlier detection with compositional data
نویسندگان
چکیده
Multivariate outlier detection is usually based on Mahalanobis distances, by plugging in robust estimates of location and covariance. For compositional data, carrying only relative information, a special transformation needs to be consulted in order to be able to work in the appropriate geometry. The effect of the transformation is discussed in this contribution. Furthermore, different possibilities for the interpretation of the identified multivariate outliers are presented.
منابع مشابه
Outlier detection for compositional data using robust methods
Outlier detection based on the Mahalanobis distance (MD) requires an appropriate transformation in case of compositional data. For the family of logratio transformations (additive, centered and isometric logratio transformation) it is shown that the MDs based on classical estimates are invariant to these transformations, and that the MDs based on affine equivariant estimators of location and co...
متن کاملCovariance-Based Outlier Detection for Compositional Data with Structural Zeros: Application to Italian Survey of Household Income and Wealth Data
Outlier detection is an important task for the statistical analysis of multivariate data, because often the outliers contain important information about the data structure. In compositional data, represented usually as proportions subject to a unit sum constraint, the ratios between the parts (variables) contain the essential information. This inherent property is, however, incompatible with th...
متن کاملIdentification of outliers types in multivariate time series using genetic algorithm
Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...
متن کاملExploratory compositional data analysis using the R-package robCompositions
Compositional data are multivariate observations that carry only relative information. This means that not the absolute values but the ratios between the variables are of interest. This is important also for an exploratory analysis of such data. We present two basic methods for the exploratory compositional data analysis (ECDA), namely multivariate outlier detection and the compositional biplot...
متن کاملMultivariate Outlier Detection Using Independent Component Analysis
The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection , data clustering and multivariate data visualization etc . Accurate identification of outliers plays an important role in statistical analysis. If classical statistical models are blindly applied to data containing outliers, the results can be ...
متن کامل