2014/01/22 by Vincent Audigier, Audigier, Vincent, François Husson +3
Mathematics · #62F10 #62F15 #62F40 #62H25 #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #msc:62F10 #msc:62F15 #msc:62F40 #msc:62H25 #stat.ME
paper · pdf · doi:10.48550/arxiv.1401.5747
16 pages
openalex publication_date 2014/01/22 · arxiv created 2015/08/19 · arxiv updated 2015/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the PCA model. Using a simulation study and real data sets, the method is compared to two classical approaches: multiple imputation based on joint modelling and on fully conditional modelling. Contrary to the others, the proposed method can be easily used on data sets where the number of individuals is less than the number of variables and when the variables are highly correlated. In addition, it provides unbiased point estimates of quantities of interest, such as an expectation, a regression coefficient or a correlation coefficient, with a smaller mean squared error. Furthermore, the widths of the confidence intervals built for the quantities of interest are often smaller whilst ensuring a valid coverage.