A principal component method to impute missing values for mixed data

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• 作者：Vincent Audigier ; François Husson…
• 关键词：Missing values ; Mixed data ; Imputation ; Principal component method ; Factorial analysis of mixed data
• 刊名：Advances in Data Analysis and Classification
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：10
• 期：1
• 页码：5-26
• 全文大小：698 KB
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• 作者单位：Vincent Audigier (1)
  François Husson (1)
  Julie Josse (1)
  
  1. Agrocampus Ouest, 65 rue de St-Brieuc, 35042, Rennes, France
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Statistics
  Statistical Theory and Methods
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Statistics for Life Sciences, Medicine and Health Sciences
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Statistics for Social Science, Behavorial Science, Education, Public Policy and Law
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1862-5355

文摘

We propose a new method to impute missing values in mixed data sets. It is based on a principal component method, the factorial analysis for mixed data, which balances the influence of all the variables that are continuous and categorical in the construction of the principal components. Because the imputation uses the principal axes and components, the prediction of the missing values is based on the similarity between individuals and on the relationships between variables. The properties of the method are illustrated via simulations and the quality of the imputation is assessed using real data sets. The method is compared to a recent method (Stekhoven and Buhlmann Bioinformatics 28:113–118, 2011) based on random forest and shows better performance especially for the imputation of categorical variables and situations with highly linear relationships between continuous variables.