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Victor Ernesto Marquez Perez Lelly María Useche Castro Dulce María Mesa Avila Ana Ides Chacon Contreras

Resumen

An imputation design is presented to combine classification and imputation in order to improve the quality of imputed datum. Imputation is done with completely randomized missing quantitative data and using regression trees. Media imputation techniques is compared, theoretical and empirically, using regression trees, in order to develop an integral classification and imputation strategy.Unbiased estimators were obtained developing the expected value of the estimator. Estimator’s proprieties were evaluated trough their variance and bias development, which showed non bias. as for the unbiased estimator variance of the media, sufficiency was not proved for the media estimator.

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Palabras Clave

missing data, imputation, CART, regression trees, unbiased estimators, simulation

Referencias
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Cómo citar
Marquez Perez, V. E., Useche Castro, L. M., Mesa Avila, D. M., & Chacon Contreras, A. I. (2017). Imputation strategy with media using regression trees. Comunicaciones En Estadística, 10(1), 9-40. https://doi.org/10.15332/s2027-3355.2017.0001.01
Sección
Artículos