Imputation strategy with media using regression trees
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...
- Autores:
-
Marquez Perez, Victor Ernesto
Useche Castro, Lelly María
Mesa Avila, Dulce María
Chacon Contreras, Ana Ides
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Repositorio Institucional USTA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/6478
- Palabra clave:
- missing data; imputation; CART; regression trees; unbiased estimators; simulation
Missing data; imputation; CART; regression trees; unbiased estimators; simulation.
- Rights
- License
- Copyright (c) 2017 Comunicaciones en Estadística
| Summary: | 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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