Missing Categorical Data Imputation and Individual Observation Level Imputation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F14%3A10282346" target="_blank" >RIV/00216208:11310/14:10282346 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61384399:31140/14:00045558

Výsledek na webu

<a href="http://dx.doi.org/10.11118/actaun201462061527" target="_blank" >http://dx.doi.org/10.11118/actaun201462061527</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/actaun201462061527" target="_blank" >10.11118/actaun201462061527</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Missing Categorical Data Imputation and Individual Observation Level Imputation

Popis výsledku v původním jazyce

Traditional missing data techniques of imputation schemes focus on prediction of the missing value based on other observed values. In the case of continuous missing data the imputation of missing values often focuses on regression models. In the case ofcategorical data, usual techniques are then focused on classification techniques which sets the missing value to the "most likely" category. This however leads to overrepresentation of the categories which are in general observed more often and hence canlead to biased results in many tasks especially in the case of presence of dominant categories. We present original methodology of imputation of missing values which results in the most likely structure (distribution) of the missing data conditional onthe observed values. The methodology is based on the assumption that the categorical variable containing the missing values has multinomial distribution. Values of the parameters of this distribution are than estimated using the multinomi

Název v anglickém jazyce

Missing Categorical Data Imputation and Individual Observation Level Imputation

Popis výsledku anglicky

Traditional missing data techniques of imputation schemes focus on prediction of the missing value based on other observed values. In the case of continuous missing data the imputation of missing values often focuses on regression models. In the case ofcategorical data, usual techniques are then focused on classification techniques which sets the missing value to the "most likely" category. This however leads to overrepresentation of the categories which are in general observed more often and hence canlead to biased results in many tasks especially in the case of presence of dominant categories. We present original methodology of imputation of missing values which results in the most likely structure (distribution) of the missing data conditional onthe observed values. The methodology is based on the assumption that the categorical variable containing the missing values has multinomial distribution. Values of the parameters of this distribution are than estimated using the multinomi

Druh

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

CEP obor

AO - Sociologie, demografie

OECD FORD obor

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Projekt

<a href="/cs/project/GAP404%2F12%2F0883" target="_blank" >GAP404/12/0883: Generační úmrtnostní tabulky České republiky: data, biometrické funkce a trendy</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

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Svazek periodika

62

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

1527-1534

Kód UT WoS článku

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EID výsledku v databázi Scopus

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