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Kernel smoothing in MATLAB : theory and practice of Kernel smoothing /
Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is giv...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
World Scientific,
2012.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 1. Introduction. 1.1. Kernels and their properties. 1.2. Use of MATLAB toolbox. 1.3. Complements
- 2. Univariate kernel density estimation. 2.1. Basic definition. 2.2. Statistical properties of the estimate. 2.3. Choosing the shape of the kernel. 2.4. Choosing the bandwidth. 2.5. Density derivative estimation. 2.6. Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order. 2.7. Boundary effects. 2.8. Simulations. 2.9. Application to real data. 2.10. Use of MATLAB toolbox. 2.11. Complements
- 3. Kernel estimation of a distribution function. 3.1. Basic definition. 3.2. Statistical properties of the estimate. 3.3. Choosing the bandwidth. 3.4. Boundary effects. 3.5. Application to data. 3.6. Simulations. 3.7. Application to real data. 3.8. Use of MATLAB toolbox. 3.9. Complements
- 4. Kernel estimation and reliability assessment. 4.1. Basic definition. 4.2. Estimation of ROC curves. 4.3. Summary indices based on the ROC curve. 4.4. Other indices of reliability assessment. 4.5. Application to real data. 4.6. Use of MATLAB toolbox
- 5. Kernel estimation of a hazard function. 5.1. Basic definition. 5.2. Statistical properties of the estimate. 5.3. Choosing the bandwidth. 5.4. Description of algorithm. 5.5. Application to real data. 5.6. Use of MATLAB toolbox. 5.7. Complements
- 6. Kernel estimation of a regression function. 6.1. Basic definition. 6.2. Statistical properties of the estimate. 6.3. Choosing the bandwidth. 6.4. Estimation of the derivative of the regression function. 6.5. Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order. 6.6. Boundary effects. 6.7. Simulations. 6.8. Application to real data. 6.9. Use of MATLAB toolbox. 6.10. Complements
- 7. Multivariate kernel density estimation. 7.1. Basic definition. 7.2. Statistical properties of the estimate. 7.3. Bandwidth matrix selection. 7.4. A special case for bandwidth selection. 7.5. Simulations. 7.6. Application to real data. 7.7. Use of MATLAB toolbox. 7.8. Complements.