  An approach to amputation, the process of introducing missing values to a
complete dataset, is presented. It allows to construct missingness indicators
in a flexible and principled way via copulas and Bernoulli margins and to
incorporate dependence in missingness patterns. Besides more classical
missingness models such as missing completely at random, missing at random, and
missing not at random, the approach is able to model structured missingness
such as block missingness and, via mixtures, monotone missingness, which are
patterns of missing data frequently found in real-life datasets. Properties
such as joint missingness probabilities or missingness correlation are derived
mathematically. The approach is demonstrated with mathematical examples and
empirical illustrations in terms of a well-known dataset.
