Robust Coefficients of Correlation or Spatial Autocorrelation Based on Implicit Weighting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00560805" target="_blank" >RIV/67985807:_____/22:00560805 - isvavai.cz</a>

Výsledek na webu

<a href="https://dx.doi.org/10.1007/s42952-022-00184-2" target="_blank" >https://dx.doi.org/10.1007/s42952-022-00184-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42952-022-00184-2" target="_blank" >10.1007/s42952-022-00184-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Coefficients of Correlation or Spatial Autocorrelation Based on Implicit Weighting

Popis výsledku v původním jazyce

Pearson product-moment correlation coefficient represents a fundamental tool for measuring linear association between two data vectors. In various applications, it is often reasonable to consider its weighted version known as the weighted correlation coefficient. This paper starts with theoretical considerations related to properties of the weighted correlation coefficient, particularly to its local robustness and relationship to other similarity measures. Inspired by the least weighted squares regression estimator, a robust correlation coefficient is investigated here together with its spatial autocorrelation extension. Finally, the considered methods are investigated in two image processing tasks.

Název v anglickém jazyce

Robust Coefficients of Correlation or Spatial Autocorrelation Based on Implicit Weighting

Popis výsledku anglicky

Pearson product-moment correlation coefficient represents a fundamental tool for measuring linear association between two data vectors. In various applications, it is often reasonable to consider its weighted version known as the weighted correlation coefficient. This paper starts with theoretical considerations related to properties of the weighted correlation coefficient, particularly to its local robustness and relationship to other similarity measures. Inspired by the least weighted squares regression estimator, a robust correlation coefficient is investigated here together with its spatial autocorrelation extension. Finally, the considered methods are investigated in two image processing tasks.

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/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Aproximativní neurovýpočty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Journal of the Korean Statistical Society

ISSN

1226-3192

e-ISSN

2005-2863

Svazek periodika

51

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

KR - Korejská republika

Počet stran výsledku

21

Strana od-do

1247-1267

Kód UT WoS článku

000844582800001

EID výsledku v databázi Scopus

2-s2.0-85137029438