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A Bootstrap Method for Using Imputation Techniques for Data With Missing Values
Author(s) -
Bello A. L.
Publication year - 1994
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710360405
Subject(s) - imputation (statistics) , missing data , statistic , statistics , computer science , standard error , data mining , mathematics
Bootstrap is a time‐honoured distribution‐free approach for attaching standard error to any statistic of interest, but has not received much attention for data with missing values especially when using imputation techniques to replace missing values. We propose a proportional bootstrap method that allows effective use of imputation techniques for all bootstrap samples. Five detcnninistic imputation techniques are examined and particular emphasis is placed on the estimation of standard error for correlation coefficient. Some real data examples are presented. Other possible applications of the proposed bootstrap method are discussed.