Imputation of missing values for compositional data using classical and robust methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F10%3A10211954" target="_blank" >RIV/61989592:15310/10:10211954 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.csda.2009.11.023" target="_blank" >http://dx.doi.org/10.1016/j.csda.2009.11.023</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csda.2009.11.023" target="_blank" >10.1016/j.csda.2009.11.023</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Imputation of missing values for compositional data using classical and robust methods

Popis výsledku v původním jazyce

New imputation algorithms for estimating missing values in compositional data are introduced. A first proposal uses the k-nearest neighbor procedure based on the Aitchison distance, a distance measure especially designed for compositional data. It is important to adjust the estimated missing values to the overall size of the compositional parts of the neighbors. As a second proposal an iterative model-based imputation technique is introduced which initially starts from the result of the proposed k-nearest neighbor procedure. The method is based on iterative regressions, thereby accounting for the whole multivariate data information. The regressions have to be performed in a transformed space, and depending on the data quality classical or robust regression techniques can be employed. The proposed methods are tested on a real and on simulated data sets. In the presence of outliers, the model-based method with robust regressions is preferable.

Název v anglickém jazyce

Imputation of missing values for compositional data using classical and robust methods

Popis výsledku anglicky

New imputation algorithms for estimating missing values in compositional data are introduced. A first proposal uses the k-nearest neighbor procedure based on the Aitchison distance, a distance measure especially designed for compositional data. It is important to adjust the estimated missing values to the overall size of the compositional parts of the neighbors. As a second proposal an iterative model-based imputation technique is introduced which initially starts from the result of the proposed k-nearest neighbor procedure. The method is based on iterative regressions, thereby accounting for the whole multivariate data information. The regressions have to be performed in a transformed space, and depending on the data quality classical or robust regression techniques can be employed. The proposed methods are tested on a real and on simulated data sets. In the presence of outliers, the model-based method with robust regressions is preferable.

Druh

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

CEP obor

BA - Obecná matematika

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název periodika

Computational Statistics & Data Analysis

ISSN

0167-9473

e-ISSN

—

Svazek periodika

54

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

3095-3107

Kód UT WoS článku

000281333900018

EID výsledku v databázi Scopus

—