Vine Copulas for Imputation of Monotone Non-response

Monotone patterns of non-response may occur in longitudinal studies. When the measured variables are dependent, it is beneficial to use their joint statistical model to impute the missing values. We propose to use vine copulas to factorise the density of the observed variables into a
cascade of bivariate copulas that yield a flexible model of their joint distribution. The structure of the vine depends on the non-response pattern.We propose a method to select the model, to estimate
the parameters of the bivariate copulas of the selected model and to impute using the constructed model. The imputed values are drawn from the conditional distribution of the missing values, given the observed data.We discuss the generalisation of our results tomore global non-response patterns.