Extremal Depth for Functional Data and Applications

We propose a new notion called "extremal depth" (ED) for functional data, discuss its properties, and compare its performance with existing concepts. The proposed notion is based on a measure of extreme "outlyingness." ED has several desirable properties that are not shared by ot...

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Vydané v:Journal of the American Statistical Association Ročník 111; číslo 516; s. 1705 - 1714
Hlavní autori: Narisetty, Naveen N., Nair, Vijayan N.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Alexandria Taylor & Francis 01.12.2016
Taylor & Francis Group,LLC
Taylor & Francis Ltd
Predmet:
Central regions
Data
Data depth
equations
Functional boxplots
Outlier detection
probability
Property
Regions
Regression analysis
Simultaneous inference
Statistics
Theory and Methods
Usefulness
ISSN:0162-1459, 1537-274X, 1537-274X
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Popis
Shrnutí:We propose a new notion called "extremal depth" (ED) for functional data, discuss its properties, and compare its performance with existing concepts. The proposed notion is based on a measure of extreme "outlyingness." ED has several desirable properties that are not shared by other notions and is especially well suited for obtaining central regions of functional data and function spaces. In particular: (a) the central region achieves the nominal (desired) simultaneous coverage probability; (b) there is a correspondence between ED-based (simultaneous) central regions and appropriate pointwise central regions; and (c) the method is resistant to certain classes of functional outliers. The article examines the performance of ED and compares it with other depth notions. Its usefulness is demonstrated through applications to constructing central regions, functional boxplots, outlier detection, and simultaneous confidence bands in regression problems. Supplementary materials for this article are available online.
Bibliografia:SourceType-Scholarly Journals-1
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ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2015.1110033