ImputeRobust-package: Multiple Imputation with Generalized Additive Models for...

Description Author(s) References


De Jong (2012), De Jong, van Buuren and Spiess (2016) introduced a new imputation method based on generalized additive models for location, scale, and shape (Rigby and Stasinopoulos, 2005), which is a class of univariate regression models, where the assumption of an exponential family is relaxed and replaced by a general distribution family. This allows the a more flexible modelling than standard parametric imputation models of not only the location (e.g. the mean), but also the scale (e.g. variance), and the shape (e.g., skewness and kurtosis) of the conditional distribution of the dependent variable given all other variables.


Daniel Salfran [email protected]

Martin Spiess [email protected]


de Jong, R., van Buuren, S. & Spiess, M. (2016) Multiple Imputation of Predictor Variables Using Generalized Additive Models. Communications in Statistics – Simulation and Computation, 45(3), 968–985.

de Jong, Roel. (2012). <e2><80><9c>Robust Multiple Imputation.<e2><80><9d> Universit<c3><a4>t Hamburg.

Rigby, R. A., and Stasinopoulos, D. M. (2005). Generalized Additive Models for Location, Scale and Shape. Journal of the Royal Statistical Society: Series C (Applied Statistics) 54 (3): 507<e2><80><93>54.

ImputeRobust documentation built on May 2, 2019, 3:27 a.m.