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christiangoueguel
ConfidenceEllipse:Computation of 2D and 3D Elliptical Joint Confidence Regions
Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.
Maintained by Christian L. Goueguel. Last updated 11 months ago.
confidence-ellipseconfidence-ellipsoidconfidence-regionmultivariate-distributionoutliers-detectionrobust-statistics
1 stars 4.70 score
aefdz
localFDA:Localization Processes for Functional Data Analysis
Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.
Maintained by Antonio Elías. Last updated 4 years ago.
classificationfunctional-data-analysisimputationoutliers-detection
2.70 score