Flexible integrated functional depths
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Publication:2214265
DOI10.3150/20-BEJ1254WikidataQ115917858 ScholiaQ115917858MaRDI QIDQ2214265
Pauliina Ilmonen, Stanislav Nagy, Sami Helander, Lauri Viitasaari, Germain Van Bever
Publication date: 7 December 2020
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.bj/1605841260
Functional data analysis (62R10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)
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- Consistency of non-integrated depths for functional data
- Integrated depth for measurable functions and sets
- Monge-Kantorovich depth, quantiles, ranks and signs
- A half-region depth for functional data
- On a notion of data depth based on random simplices
- Curves discrimination: a nonparametric functional approach
- Robust estimation and classification for functional data via projection-based depth notions
- On depth measures and dual statistics. A methodology for dealing with general data
- Log log laws for empirical measures
- Breakdown properties of location estimates based on halfspace depth and projected outlyingness
- Multivariate analysis by data depth: Descriptive statistics, graphics and inference. (With discussions and rejoinder)
- Data depth for measurable noisy random functions
- Rates of convergence in the central limit theorem for empirical processes
- General notions of statistical depth function.
- Trimmed means for functional data
- Asymptotics for the Tukey depth process, with an application to a multivariate trimmed mean
- Weak convergence and empirical processes. With applications to statistics
- Computing the halfspace depth with multiple try algorithm and simulated annealing algorithm
- Spatial depth-based classification for functional data
- Multivariate quantiles and multiple-output regression quantiles: from \(L_{1}\) optimization to halfspace depth
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- The spatial distribution in infinite dimensional spaces and related quantiles and depths
- Concerns with functional depth
- Integrated depth for functional data: statistical properties and consistency
- Fast Computation of Tukey Trimmed Regions and Median in Dimension p > 2
- Functional Classification in Hilbert Spaces
- A limit theorem for measurable random processes and its applications
- Uniform Central Limit Theorems
- A Functional Data—Analytic Approach to Signal Discrimination
- DD-Classifier: Nonparametric Classification Procedure Based onDD-Plot
- Multivariate Functional Halfspace Depth
- On the Concept of Depth for Functional Data
- Nearest neighbor classification in infinite dimension
- On Maximum Depth and Related Classifiers
- Achieving near Perfect Classification for Functional Data
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