Multivariate functional outlier detection
Functional data are occurring more and more often in practice, and various statistical
techniques have been developed to analyze them. In this paper we consider multivariate …
techniques have been developed to analyze them. In this paper we consider multivariate …
Review on functional data classification
A fundamental problem in functional data analysis is to classify a functional observation
based on training data. The application of functional data classification has gained immense …
based on training data. The application of functional data classification has gained immense …
Curve boxplot: Generalization of boxplot for ensembles of curves
M Mirzargar, RT Whitaker… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
In simulation science, computational scientists often study the behavior of their simulations
by repeated solutions with variations in parameters and/or boundary values or initial …
by repeated solutions with variations in parameters and/or boundary values or initial …
Directional outlyingness for multivariate functional data
The direction of outlyingness is crucial to describing the centrality of multivariate functional
data. Motivated by this idea, classical depth is generalized to directional outlyingness for …
data. Motivated by this idea, classical depth is generalized to directional outlyingness for …
Polynomial chaos expansion of random coefficients and the solution of stochastic partial differential equations in the tensor train format
We apply the tensor train (TT) decomposition to construct the tensor product polynomial
chaos expansion (PCE) of a random field, to solve the stochastic elliptic diffusion PDE with …
chaos expansion (PCE) of a random field, to solve the stochastic elliptic diffusion PDE with …
Integrated depth for functional data: statistical properties and consistency
Several depths suitable for infinite-dimensional functional data that are available in the
literature are of the form of an integral of a finite-dimensional depth function. These …
literature are of the form of an integral of a finite-dimensional depth function. These …
Surface boxplots
In this paper, we introduce a surface boxplot as a tool for visualization and exploratory
analysis of samples of images. First, we use the notion of volume depth to order the images …
analysis of samples of images. First, we use the notion of volume depth to order the images …
Functional data analysis for the detection of outliers and study of the effects of the COVID-19 pandemic on air quality: a case study in Gijón, Spain
Air pollution, especially at the ground level, poses a high risk for human health as it can
have serious negative effects on the population of certain areas. The high variability of this …
have serious negative effects on the population of certain areas. The high variability of this …
An overview of consistency results for depth functionals
S Nagy - Functional statistics and related fields, 2017 - Springer
Data depth is a nonparametric tool which may serve as an extension of quantiles to general
data. Any viable depth must posses the uniform strong consistency property of its sample …
data. Any viable depth must posses the uniform strong consistency property of its sample …
Two-sample tests for multivariate functional data with applications
Z Qiu, J Chen, JT Zhang - Computational Statistics & Data Analysis, 2021 - Elsevier
Multivariate functional data are frequently obtained in many scientific or industrial areas
where several functions for a statistical unit are observed over time. It is often interesting to …
where several functions for a statistical unit are observed over time. It is often interesting to …