Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F18%3A73589105" target="_blank" >RIV/61989592:15310/18:73589105 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/02664763.2017.1410524" target="_blank" >http://dx.doi.org/10.1080/02664763.2017.1410524</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02664763.2017.1410524" target="_blank" >10.1080/02664763.2017.1410524</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

Popis výsledku v původním jazyce

The logratio methodology is not applicable when rounded zeros occur in compositional data. There are many methods to deal with rounded zeros. However, some methods are not suitable for analyzing data sets with high dimensionality. Recently, related methods have been developed, but they cannot balance the calculation time and accuracy. For further improvement, we propose a method based on regression imputation with Q-mode clustering. This method forms the groups of parts and builds partial least squares regression with these groups using centered logratio coordinates.We also prove that using centered logratio coordinates or isometric logratio coordinates in the response of partial least squares regression have the equivalent results for the replacement of rounded zeros. Simulation study and real example are conducted to analyze the performance of the proposed method. The results show that the proposed method can reduce the calculation time in higher dimensions and improve the quality of results.

Název v anglickém jazyce

Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

Popis výsledku anglicky

The logratio methodology is not applicable when rounded zeros occur in compositional data. There are many methods to deal with rounded zeros. However, some methods are not suitable for analyzing data sets with high dimensionality. Recently, related methods have been developed, but they cannot balance the calculation time and accuracy. For further improvement, we propose a method based on regression imputation with Q-mode clustering. This method forms the groups of parts and builds partial least squares regression with these groups using centered logratio coordinates.We also prove that using centered logratio coordinates or isometric logratio coordinates in the response of partial least squares regression have the equivalent results for the replacement of rounded zeros. Simulation study and real example are conducted to analyze the performance of the proposed method. The results show that the proposed method can reduce the calculation time in higher dimensions and improve the quality of results.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF APPLIED STATISTICS

ISSN

0266-4763

e-ISSN

—

Svazek periodika

45

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

2067-2080

Kód UT WoS článku

000436973300009

EID výsledku v databázi Scopus

2-s2.0-85049522544