Functional outlier detection and taxonomy by sequential transformations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F20%3A43900841" target="_blank" >RIV/60076658:12510/20:43900841 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0167947320300517?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167947320300517?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csda.2020.106960" target="_blank" >10.1016/j.csda.2020.106960</a>

Result language

angličtina

Original language name

Functional outlier detection and taxonomy by sequential transformations

Original language description

Functional data analysis can be seriously impaired by abnormal observations, which can be classified as either magnitude or shape outliers based on their way of deviating from the bulk of data. Identifying magnitude outliers is relatively easy, while detecting shape outliers is much more challenging. We propose turning the shape outliers into magnitude outliers through data transformation and detecting them using the functional boxplot. Besides easing the detection procedure, applying several transformations sequentially provides a reasonable taxonomy for the flagged outliers. A joint functional ranking, which consists of several transformations, is also defined here. Simulation studies are carried out to evaluate the performance of the proposed method using different functional depth notions. Interesting results are obtained in several practical applications.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computational Statistics &amp; Data Analysis

ISSN

0167-9473

e-ISSN

—

Volume of the periodical

2020

Issue of the periodical within the volume

149

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

17

Pages from-to

1-17

UT code for WoS article

000531596000004

EID of the result in the Scopus database

2-s2.0-85083312381