Functional outlier detection and taxonomy by sequential transformations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Functional outlier detection and taxonomy by sequential transformations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Functional outlier detection and taxonomy by sequential transformations

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Computational Statistics &amp; Data Analysis

ISSN

0167-9473

e-ISSN

—

Svazek periodika

2020

Číslo periodika v rámci svazku

149

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

000531596000004

EID výsledku v databázi Scopus

2-s2.0-85083312381