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Influence of outliers on some multiple imputation methods
ID
Quintano, Claudio
(
Avtor
),
ID
Castellano, Rosalia
(
Avtor
),
ID
Rocca, Antonella
(
Avtor
)
URL - Predstavitvena datoteka, za dostop obiščite
http://mrvar.fdv.uni-lj.si/pub/mz/mz7.1/quintano.pdf
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Izvleček
In the field of data quality, imputation is the most used method for handling missing data. The performance of imputation techniques is influenced by various factors, especially when data represent only a sample of population, for example the survey design characteristics. In this paper, we compare the results of different multiple imputation methods in terms of final estimates when outliers occur in a dataset. Consequently, in order to evaluate the influence of outliers on the performance of these methods, the procedure is applied before and after that we have identified and removed them. For this purpose, missing data were simulated on data coming from sample ISTAT annual survey on Small and Medium Enterprises. MAR mechanism is assumed for missing data. The methods are based on the multiple imputation through the Markov Chain Monte Carlo (MCMC), the propensity score and the mixture models. The results highlight the strong influence of data characteristics on final estimates.
Jezik:
Angleški jezik
Vrsta gradiva:
Delo ni kategorizirano
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FDV - Fakulteta za družbene vede
Leto izida:
2010
Št. strani:
Str. 1-16
Številčenje:
Vol. 7, no. 1
PID:
20.500.12556/RUL-23136
UDK:
303
ISSN pri članku:
1854-0023
COBISS.SI-ID:
29643869
Datum objave v RUL:
11.07.2014
Število ogledov:
1620
Število prenosov:
214
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Objavi na:
Gradivo je del revije
Naslov:
Advances in methodology and statistics
Skrajšan naslov:
Metodol. zv.
Založnik:
Fakulteta za družbene vede
ISSN:
1854-0023
COBISS.SI-ID:
215795712
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