  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.
