ImputeRobust-package: Multiple Imputation with Generalized Additive Models for...
In dsalfran/ImputeRobust: Robust Multiple Imputation with Generalized Additive Models for Location Scale and Shape
Description
Author(s)
References
Description
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.
Author(s)
Daniel Salfran daniel.salfran@uni-hamburg.de
Martin Spiess martin.spiess@uni-hamburg.de
References
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). “Robust Multiple Imputation.” Universität
Hamburg. http://ediss.sub.uni-hamburg.de/volltexte/2012/5971/.
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): 507–54.
dsalfran/ImputeRobust documentation built on May 15, 2019, 2:57 p.m.
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Description Author(s) References
Description
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.
Author(s)
Daniel Salfran daniel.salfran@uni-hamburg.de
Martin Spiess martin.spiess@uni-hamburg.de
References
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). “Robust Multiple Imputation.” Universität Hamburg. http://ediss.sub.uni-hamburg.de/volltexte/2012/5971/.
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): 507–54.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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