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Teaching GuideTerm Faculty of Computer Science |
| Mestrado Universitario en Técnicas Estadísticas (Plan 2019) |
 Subjects |
| Resampling Techniques |
| Contents |
| Identifying Data | 2019/20 | |||||||||||||
| Subject | Resampling Techniques | Code | 614493022 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 1st four-month period |
First Second | Optional | 5 | ||||||||||
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| Topic | Sub-topic |
| 1. Motivation of the Bootstrap principle. | Uniform bootstrap. Bootstrap distribution calculation: exact distribution and Monte Carlo approximation. Examples. |
| 2. Some applications of the Bootstrap method. | Application of the Bootstrap to estimate the precision and the bias of an estimator. Examples. |
| 3. Motivation of the Jackknife method. | Jackknife estimation of the precision and the bias of an estimator. Bootstrap/Jackknife relationship. Examples. Simulation studies. |
| 4. Variations of the uniform Bootstrap. | Parametric Bootstrap, symmetrized Bootstrap, smoothed Bootstrap, weighted Bootstrap and biased Bootstrap. Discussion and examples. Validity of the Bootstrap approach. Examples. |
| 5. Applications of Bootstrap to construct confidence intervals. | Percentile method, percentile-t method, symmetrized percentile-t method . Examples. Simulation studies. |
| 6. Bootstrap and nonparametric density estimation. | Bootstrap approximation for the distribution of the Parzen-Rosenblatt estimator. The Bootstrap in the selection of the smoothing parameter. |
| 7. Bootstrap and nonparametric estimation of the regression function. | Bootstrap approximation of the distribution of the Nadaraya-Watson estimator. Different resampling methods and results. |
| 8. Bootstrap with censored data. | Introduction to censored data. Bootstrap resampling plans in the presence of censorship. Relations among them. |
| 9. Bootstrap with dependent data. | Introduction to the usual conditions of dependency and dependent data models. Parametric models of dependence. General dependence situations: Moving Block Bootstrap, Stationary Bootstrap and Subsampling method. |
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