Missing Categorical Data Imputation and Individual Observation Level Imputation

Result code in 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>

Alternative codes found

RIV/61384399:31140/14:00045558

Result on the web

<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>

Result language

angličtina

Original language name

Missing Categorical Data Imputation and Individual Observation Level Imputation

Original language description

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

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

AO - Sociology, demography

OECD FORD branch

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Project

<a href="/en/project/GAP404%2F12%2F0883" target="_blank" >GAP404/12/0883: Cohort life tables for the Czech Republic: data, biometric functions, and trends</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2014

Confidentiality

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

Name of the periodical

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

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Volume of the periodical

62

Issue of the periodical within the volume

6

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

8

Pages from-to

1527-1534

UT code for WoS article

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EID of the result in the Scopus database

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