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An Empirical Comparison of Global and Local Functional Depths
A functional data depth provides a center-outward ordering criterion that allows the definition of measures such as median, trimmed means, central...
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Tree-based boosting with functional data
In this article we propose a boosting algorithm for regression with functional explanatory variables and scalar responses. The algorithm uses...
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Shape-based functional data analysis
Functional data analysis (FDA) is a fast-growing area of research and development in statistics. While most FDA literature imposes the classical
... 
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Model-based clustering of functional data via mixtures of t distributions
We propose a procedure, called T-funHDDC, for clustering multivariate functional data with outliers which extends the functional high dimensional...
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Localization processes for functional data analysis
We propose an alternative to k -nearest neighbors for functional data whereby the approximating neighboring curves are piecewise functions built from...
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A notion of depth for sparse functional data
Data depth is a well-known and useful nonparametric tool for analyzing functional data. It provides a novel way of ranking a sample of curves from...
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Detecting and classifying outliers in big functional data
We propose two new outlier detection methods, for identifying and classifying different types of outliers in (big) functional data sets. The proposed...
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Robust archetypoids for anomaly detection in big functional data
Archetypoid analysis (ADA) has proven to be a successful unsupervised statistical technique to identify extreme observations in the periphery of the...
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A new way for ranking functional data with applications in diagnostic test
This is a two faces paper. Firstly, it investigates diagnostic tests in situations when the observed variables are functional, that is, diagnostic...
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Distribution-free Pointwise Adjusted %-values for Functional Hypotheses
Graphical tests assess whether a function of interest departs from an envelope of functions generated under a simulated null distribution. This...
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Theory of angular depth for classification of directional data
Depth functions offer an array of tools that enable the introduction of quantile- and ranking-like approaches to multivariate and non-Euclidean...
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Level sets of depth measures in abstract spaces
The lens depth of a point has been recently extended to general metric spaces, which is not the case for most depths. It is defined as the...
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A New Method for Ordering Functional Data and its Application to Diagnostic Test
This contribution proposes a new ordering method for functional data which could be a starting point for develo** new advances in problems for...
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An introduction to the (postponed) 5th edition of the International Workshop on Functional and Operatorial Statistics
This volume is composed by a set of short papers corresponding to some of the contributions that were sent to be presented at the fifth edition of...
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An integrated local depth measure
We introduce the Integrated Dual Local Depth, which is a local depth measure for data in a Banach space based on the use of one-dimensional...
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Depth in Infinite-dimensional Spaces
Depth is a statistical tool that aims to introduce sensible data-dependent ordering of points in multivariate / function spaces. In theory, this...
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Robustness of the deepest projection regression functional
Depth notions in regression have been systematically proposed and examined in Zuo ( ar**v:1805.02046 ,
2018 ). One of the prominent advantages of the...
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The Halfspace Depth Characterization Problem
The halfspace depth characterization conjecture states that for any two distinct (probability) measures P and Q in the d-dimensional Euclidean space,...
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The zonoid region parameter depth
A new concept of depth for central regions is introduced. The proposed depth notion assesses how well an interval fits a given univariate...
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Halfspace depth does not characterize probability distributions
We give examples of different multivariate probability distributions whose halfspace depths coincide at all points of the sample space.
