Sparse Functional Boxplots for Multivariate Curves

TL;DR

This paper develops new sparse functional boxplot tools for visualizing and detecting outliers in sparse multivariate functional data, applicable to both complete and sparse datasets, with demonstrated effectiveness through simulations and health data applications.

Contribution

It introduces the sparse functional boxplot and intensity sparse functional boxplot, extending traditional methods to sparse and multivariate functional data, and improves data fitting and outlier detection techniques.

Findings

Effective visualization of sparse multivariate data.

Enhanced outlier detection in sparse functional data.

Validated methods with simulations and health datasets.

Abstract

This paper introduces the sparse functional boxplot and the intensity sparse functional boxplot as practical exploratory tools. Besides being available for complete functional data, they can be used in sparse univariate and multivariate functional data. The sparse functional boxplot, based on the functional boxplot, displays sparseness proportions within the 50\% central region. The intensity sparse functional boxplot indicates the relative intensity of fitted sparse point patterns in the central region. The two-stage functional boxplot, which derives from the functional boxplot to detect outliers, is furthermore extended to its sparse form. We also contribute to sparse data fitting improvement and sparse multivariate functional data depth. In a simulation study, we evaluate the goodness of data fitting, several depth proposals for sparse multivariate functional data, and compare the results of outlier detection between the sparse functional boxplot and its two-stage version. The practical applications of the sparse functional boxplot and intensity sparse functional boxplot are illustrated with two public health datasets. Supplementary materials and codes are available for readers to apply our visualization tools and replicate the analysis.