The Centre for Multilevel Modelling has a long-standing interest in developing methods and software to ... at any level / classification)* Normal, binomial, Poisson, negative binomial† Imputation ...

and how this classification will affect our procedures for dealing with the missing data. Next, we will briefly cover early methods for handling missing data, such as complete case analysis and single ...

This dissertation focuses on restrictive imputation methods for survey data. Missing data form an ubiquitous source of problems that most scientists or researchers cannot escape. For example, in ...

We develop an imputation method that uses the Dirichlet distribution to model the data. This method is convenient because of its flexibility. This procedure can impute data items that are non-negative ...

This course will introduce you to advanced multiple imputation methods that have been developed to address complex missing data analyses. This course builds on prior knowledge of multiple imputation ...

When the degree of missingness in the data raises questions about the validity of preferred methods such as model-based multiple imputation, they recommend that sensitivity analyses be performed ...