Comparison of nonparametric estimators versus parametric for reliability function
One of the main objectives of the area of realiability is to estimate the function of reliability, where traditionally are used non parametric estimators, being more efficient in sample of big sizes. In this work nonparametric estimators are compared to the reliability function through the mean squa...
- Autores:
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_6662
- Fecha de publicación:
- 2015
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/12250
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/ingenieria_sogamoso/article/view/4246
https://repositorio.uptc.edu.co/handle/001/12250
- Palabra clave:
- bootstrap
reliability
nonparametric estimators.
bootstrap
confiabilidad
estimadores no paramétricos
- Rights
- License
- Derechos de autor 2015 Ingeniería Investigación y Desarrollo
| Summary: | One of the main objectives of the area of realiability is to estimate the function of reliability, where traditionally are used non parametric estimators, being more efficient in sample of big sizes. In this work nonparametric estimators are compared to the reliability function through the mean square error using nonparametric estimators of Kaplan & Meier (1958), Nelson estimator (1969) and Bootstrap applied to Kaplan & Meier and Nelson. The comparison is made considering the parametric estimates, through simulation with different scenarios, times of interest, sizes sample and percentages of censorship, showing that the Bootstrap resampling normal type does not present the best results with Kaplan & Meier. Using Nelson, the 18% was more efficient. |
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