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- W3201958446 abstract "Missing data occurs when some values are not stored or observed for variables of interest. However, most of the statistical theory assumes that data is fully observed. An alternative to deal with incomplete databases is to fill in the spaces corresponding to the missing information based on some criteria, this technique is called imputation. We introduce a new imputation methodology for databases with non-response units using additional information from fully observed auxiliary variables. We assume that the non-observed variables are continuous, and that auxiliary variables assist to improve the imputation capacity of the model. In a fully Bayesian framework, our method uses a flexible mixture of multivariate normal distributions to model the response and the auxiliary variables jointly. Under this framework, we use the properties of Gaussian Cluster-Weighted modeling to construct a predictive model to impute the missing values using the information from the covariates. Simulations studies and a real data illustration are presented to show the method imputation capacity under a variety of scenarios and in comparison to other literature methods." @default.
- W3201958446 created "2021-10-11" @default.
- W3201958446 creator A5042819493 @default.
- W3201958446 date "2021-03-26" @default.
- W3201958446 modified "2023-09-26" @default.
- W3201958446 title "Imputation of missing data using gaussian linear Cluster-Weighted Modeling" @default.
- W3201958446 hasPublicationYear "2021" @default.
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