Bootstrap Methods for a Measurement Estimation Problem
In this paper, a new approach for the statistical characterization of a measurand is presented. A description of how different bootstrap techniques can be applied in practice to estimate successfully a measurand probability density function (pdf) is given. When the direct observation of a quantity o...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2006 |
| País: | México |
| Institución: | Universidad Autónoma de Zacatecas |
| Repositorio: | Repositorio Institucional Caxcán |
| Idioma: | inglés |
| OAI Identifier: | oai:http://ricaxcan.uaz.edu.mx:20.500.11845/1654 |
| Acceso en línea: | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1654 https://doi.org/10.48779/98et-sw82 |
| Access Level: | acceso abierto |
| Palabra clave: | INGENIERIA Y TECNOLOGIA [7] Bootstrap indirect measurement Monte Carlo simulation nonlinear regression |
| Sumario: | In this paper, a new approach for the statistical characterization of a measurand is presented. A description of how different bootstrap techniques can be applied in practice to estimate successfully a measurand probability density function (pdf) is given. When the direct observation of a quantity of interest is practically impossible such as in nondestructive testing, it is necessary to estimate such quantity, which is also called measurand. The statistical characterization of any estimator is important, because all the uncertainty features can be accessible to qualify such estimator. On the other hand, most of the time, the large-scale repetition of an experiment is not economically feasible, so that the Monte Carlo methods cannot be used directly for uncertainty characterization. |
|---|