Add to ORKGA depth for multivariate functional data is defined and studied. By the multi-variate nature and by including a weight function, it acknowledges important characteristics of functional data, namely differences in the amount of local amplitude, shape and phase variation. Both population and finite sample versions are studied. The multivariate sample of curves may include warping functions, derivatives and integrals of the original curves for a better overall representation of the functional data via the depth. A simulation study and data examples confirm the good performance of this depth function
A recent and highly attractive area of research in statistics is the analysis of functional data. In...
There has been extensive work on data depth-based methods for robust multivariate data analysis. Rec...
Two frameworks for multivariate functional depth based on multivariate depths are introduced in this...
A multivariate depth for functional data is defined and studied. By the multivariate nature and by i...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
The statistical analysis of functional data is a growing need in many research areas. We propose a n...
Statistical depth functions became well known nonparametric tool of multivariate data analyses. The ...
A new definition of depth for functional observations is introduced based on the notion of "half-reg...
For multivariate data, the halfspace depth function can be seen as a natural and affine equivariant ...
In this thesis the theory of depth functions is researched. Depth functions are functions that measu...
We propose robust inference tools for functional data based on the notion of depth for curves. We ex...
A data depth measures the centrality of a point with respect to an empirical distribution. Postulate...
Classification is an important task when data are curves. Recently, the notion of statistical depth ...
A new definition of depth for functional observations is introduced based on the notion of “half-reg...
Classification is an important task when data are curves. Recently, the notion of statistical depth ...
A recent and highly attractive area of research in statistics is the analysis of functional data. In...
There has been extensive work on data depth-based methods for robust multivariate data analysis. Rec...
Two frameworks for multivariate functional depth based on multivariate depths are introduced in this...
A multivariate depth for functional data is defined and studied. By the multivariate nature and by i...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
The statistical analysis of functional data is a growing need in many research areas. We propose a n...
Statistical depth functions became well known nonparametric tool of multivariate data analyses. The ...
A new definition of depth for functional observations is introduced based on the notion of "half-reg...
For multivariate data, the halfspace depth function can be seen as a natural and affine equivariant ...
In this thesis the theory of depth functions is researched. Depth functions are functions that measu...
We propose robust inference tools for functional data based on the notion of depth for curves. We ex...
A data depth measures the centrality of a point with respect to an empirical distribution. Postulate...
Classification is an important task when data are curves. Recently, the notion of statistical depth ...
A new definition of depth for functional observations is introduced based on the notion of “half-reg...
Classification is an important task when data are curves. Recently, the notion of statistical depth ...
A recent and highly attractive area of research in statistics is the analysis of functional data. In...
There has been extensive work on data depth-based methods for robust multivariate data analysis. Rec...
Two frameworks for multivariate functional depth based on multivariate depths are introduced in this...