Depth-Based Recognition of Shape Outlying Functions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10365500" target="_blank" >RIV/00216208:11320/17:10365500 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/10618600.2017.1336445" target="_blank" >http://dx.doi.org/10.1080/10618600.2017.1336445</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/10618600.2017.1336445" target="_blank" >10.1080/10618600.2017.1336445</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Depth-Based Recognition of Shape Outlying Functions

Popis výsledku v původním jazyce

A major drawback of many established depth functionals is their ineffectiveness in identifying functions outlying merely in shape. Herein, a simple modification of functional depth is proposed to provide a remedy for this difficulty. The modification is versatile, widely applicable, and introduced without imposing any assumptions on the data, such as differentiability. It is shown that many favorable attributes of the original depths for functions, including consistency properties, remain preserved for the modified depths. The powerfulness of the new approach is demonstrated on a number of examples for which the known depths fail to identify the outlying functions.

Název v anglickém jazyce

Depth-Based Recognition of Shape Outlying Functions

Popis výsledku anglicky

A major drawback of many established depth functionals is their ineffectiveness in identifying functions outlying merely in shape. Herein, a simple modification of functional depth is proposed to provide a remedy for this difficulty. The modification is versatile, widely applicable, and introduced without imposing any assumptions on the data, such as differentiability. It is shown that many favorable attributes of the original depths for functions, including consistency properties, remain preserved for the modified depths. The powerfulness of the new approach is demonstrated on a number of examples for which the known depths fail to identify the outlying functions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA14-07234S" target="_blank" >GA14-07234S: Mnohorozměrné regresní kvantily v ekonometrii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Journal of Computational and Graphical Statistics

ISSN

1061-8600

e-ISSN

—

Svazek periodika

2017

Číslo periodika v rámci svazku

26 (4)

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

883-893

Kód UT WoS článku

000423019700018

EID výsledku v databázi Scopus

2-s2.0-85031408669