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- W2121657366 abstract "In the field of data quality, imputation is the mo st used method for handling missing data. The performance of imputatio n techniques is influenced by various factors, especially when data represent only a sample of population, for example the survey design charac teristics. In this paper, we compare the results of different multiple imputa tion methods in terms of final estimates when outliers occur in a dataset. C onsequently, in order to evaluate the influence of outliers on the performan ce 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 da ta 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 th rough 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." @default.
- W2121657366 created "2016-06-24" @default.
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- W2121657366 date "2010-01-01" @default.
- W2121657366 modified "2023-09-27" @default.
- W2121657366 title "Influence of Outliers on Some Multiple Imputation Methods" @default.
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