Classical and robust orthogonal regression between parts of compositional data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F16%3A33159845" target="_blank" >RIV/61989592:15310/16:33159845 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/02331888.2016.1162164" target="_blank" >http://dx.doi.org/10.1080/02331888.2016.1162164</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02331888.2016.1162164" target="_blank" >10.1080/02331888.2016.1162164</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classical and robust orthogonal regression between parts of compositional data

Popis výsledku v původním jazyce

The different parts (variables) of a compositional data set cannot be considered independent from each other, since only the ratios between the parts constitute the relevant information to be analysed. Practically, this information can be included in a system of orthonormal coordinates. For the task of regression of one part on other parts, a specific choice of orthonormal coordinates is proposed which allows for an interpretation of the regression parameters in terms of the original parts. In this context, orthogonal regression is appropriate since all compositional parts - also the explanatory variables - are measured with errors. Besides classical (least-squares based) parameter estimation, also robust estimation based on robust principal component analysis is employed. Statistical inference for the regression parameters is obtained by bootstrap; in the robust version the fast and robust bootstrap procedure is used. The methodology is illustrated with a data set from macroeconomics.

Název v anglickém jazyce

Classical and robust orthogonal regression between parts of compositional data

Popis výsledku anglicky

The different parts (variables) of a compositional data set cannot be considered independent from each other, since only the ratios between the parts constitute the relevant information to be analysed. Practically, this information can be included in a system of orthonormal coordinates. For the task of regression of one part on other parts, a specific choice of orthonormal coordinates is proposed which allows for an interpretation of the regression parameters in terms of the original parts. In this context, orthogonal regression is appropriate since all compositional parts - also the explanatory variables - are measured with errors. Besides classical (least-squares based) parameter estimation, also robust estimation based on robust principal component analysis is employed. Statistical inference for the regression parameters is obtained by bootstrap; in the robust version the fast and robust bootstrap procedure is used. The methodology is illustrated with a data set from macroeconomics.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Statistics : a journal of theoretical and applied statistics

ISSN

0233-1888

e-ISSN

—

Svazek periodika

50

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

1261-1275

Kód UT WoS článku

000385543000005

EID výsledku v databázi Scopus

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