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Author
Misztal Małgorzata (University of Lodz, Poland)
Title
Imputation of Missing Data Using R Package
Imputacja brakujących danych z wykorzystaniem środowiska R
Source
Acta Universitatis Lodziensis. Folia Oeconomica, 2012, t. 269, s. 131-144, tab., rys., bibliogr. 12 poz.
Issue title
Multivariate Statistical Analysis : Methodological Aspects and Applications
Keyword
Analiza statystyczna, Analiza danych, Programy komputerowe
Statistical analysis, Data analysis, Computer programs
Note
summ., streszcz.
Abstract
W praktycznych zastosowaniach metod statystycznych często pojawia się problem występowania w zbiorach danych brakujących wartości. W takich sytuacjach wykorzystać można metody imputacji danych, polegające na zastąpieniu brakujących danych konkretnymi wartościami w celu uzyskania kompletnego zbioru danych. W referacie dokonano przeglądu metod imputacji danych oraz opisano możliwości wykonania koniecznych obliczeń z wykorzystaniem dostępnych w środowisku R pakietów realizujących procedury imputacji jednostkowej i wielokrotnej. (abstrakt oryginalny)

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. (original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
The Main Library of the Wroclaw University of Economics
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Bibliography
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  1. Allison P. D. (2002), Missing data, Series: Quantitative Applications in the Social Sciences 07-136, SAGE Publications, Thousand Oaks, London, New Delhi.
  2. Ambler G., Omar R. Z., Royston P. (2007), A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome, "Statistical Methods in Medical Research" 2007; 16: 277-298.
  3. Crookston N. L., Finley A. O. (2008), yaImpute: An R Package for kNN Imputation, "Journal of Statistical Software", January 2008, Volume 23, Issue 10.
  4. Horton N. J., Kleinman K. P. (2007), Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models, "The American Statistician" 2007,6(1): 79-90.
  5. Kenward M. G., Carpenter J. (2007), Multiple imputation: current perspectives, "Statistical Methods in Medical Research" 2007; 16: 199-218.
  6. Little R. J. A., Rubin D. B. (2002), Statistical Analysis with Missing Data, Wiley, New Jersey.
  7. Molenberghs G., Kenward M. G (2007), Missing Data in Clinical Studies, Wiley, England.
  8. Schafer J. L. (1996), Analysis of Incomplete Multivariate Data, Chapman & Hall, New York.
  9. Su Y.-S., Gelman A., Hill J., Yajima M. (2011), Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box, "Journal of Statistical Software", in press.
  10. van Buuren S., Groothuis-Oudshoorn K. (2011), MCE: Multivariate Imputation by Chained Equations in R, "Journal of Statistical Software", in press.
  11. Wayman J. C. (2003), Multiple Imputation for Missing Data: What Is It And How Can I Use It?, http://www.csos.jhu.edu/contact/staff/jwayman_pub/wayman_multimp_aera2003.pdf.
  12. Yu L.-M., Burton A., Rivero-Arias O. (2007), Evaluation of software for multiple imputation of semi-continuous data, "Statistical Methods in Medical Research" 2007; 16: 243-258.
Cited by
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ISSN
0208-6018
Language
eng
URI / DOI
http://hdl.handle.net/11089/1893
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