Multivariate techniques for imputation based on Bayesian networks

Title:

Multivariate techniques for imputation based on Bayesian networks

Creator:

Scanu , Mauro, Vicard , Paola, Di Zio, Marco, and Sacco, Guiseppe

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:908db59c-e837-401c-8d5e-60695bacc17f
uuid:908db59c-e837-401c-8d5e-60695bacc17f

Subject:

Missing data, graphical mmodels, and multivariate analysis

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, we compare two imputation procedures based on Bayesian networks. One method imputes missing items of a variable taking advantage only on information of its parents, while the other takes advantage of its Markov blanket. The structure of the paper is as follows. The first section contains an illustration of Bayesian networks. Then, we explain how to use the information contained in Bayesian networks in Section 2. In Section 3, we describe two evaluation indicators of imputation procedures. Finally, a Monte Carlo evaluation is carried on a real data set in Section 4.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:4

Harvested from:

CDK

Metadata only:

false