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Imputation of Missing Data Using R Package

100%
Misztal M., Uniwersytet Łódzki; Wydział Ekonomiczno-Socjologiczny; Instytut Statystyki i Demografii
Acta Universitatis Lodziensis. Folia Oeconomica
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2012
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vol. 269
EN
Missing data are quite common in practical applications of statistical methods. Imputation is general statistical method for the analysis of incomplete data sets. The goal of the paper is to review selected imputation techniques. Special attention is paid to methods implemented in some packages working in the R environment. An example is presented to show how to handle missing values using a few procedures of single and multiple imputation implemented in R.
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Review of methods for data sets with missing values and practical applications

45%
Korczyński A.
Śląski Przegląd Statystyczny
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2014
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issue 12(18)
83-104
EN
The aim of this paper is to revise the traditional methods (complete-case analysis, available-case analysis, single imputation) and current methods (likelihood-based methods, multiple imputation, weighting methods) for handling the problem of missing data and to assess their usefulness in statistical research. The paper provides the terminology and the description of traditional and current methods and algorithms used in the analysis of incomplete data sets. The methods are assessed in terms of the statistical properties of their estimators. An example is provided for the multiple imputation method. The review indicates that current methods outweigh traditional ones in terms of bias reduction, precision and efficiency of the estimation.
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