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Functional estimation for density, regression models and processes /
This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators fo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
World Scientific,
©2011.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 1. Introduction. 1.1. Estimation of a density. 1.2. Estimation of a regression curve. 1.3. Estimation of functionals of processes. 1.4. Content of the book
- 2. Kernel estimator of a density. 2.1. Introduction. 2.2. Risks and optimal bandwidths for the kernel estimator. 2.3. Weak convergence. 2.4. Minimax and histogram estimators. 2.5. Estimation of functionals of a density. 2.6. Density of absolutely continuous distributions. 2.7. Hellinger distance between a density and its estimator. 2.8. Estimation of the density under right-censoring. 2.9. Estimation of the density of left-censored variables. 2.10. Kernel estimator for the density of a process. 2.11. Exercises
- 3. Kernel estimator of a regression function. 3.1. Introduction and notation. 3.2. Risks and convergence rates for the estimator. 3.3. Optimal bandwidths. 3.4. Weak convergence of the estimator. 3.5. Estimation of a regression curve by local polynomials. 3.6. Estimation in regression models with functional variance. 3.7. Estimation of the mode of a regression function. 3.8. Estimation of a regression function under censoring. 3.9. Proportional odds model. 3.10. Estimation for the regression function of processes. 3.11. Exercises
- 4. Limits for the varying bandwidths estimators. 4.1. Introduction. 4.2. Estimation of densities. 4.3. Estimation of regression functions. 4.4. Estimation for processes. 4.5. Exercises
- 5. Nonparametric estimation of quantiles. 5.1. Introduction. 5.2. Asymptotics for the quantile processes. 5.3. Bandwidth selection. 5.4. Estimation of the conditional density of Y given X. 5.5. Estimation of conditional quantiles for processes. 5.6. Inverse of a regression function. 5.7. Quantile function of right-censored variables. 5.8. Conditional quantiles with variable bandwidth. 5.9. Exercises
- 6. Nonparametric estimation of intensities for stochastic processes. 6.2. Introduction. 6.2. Risks and convergences for estimators of the intensity. 6.3. Risks and convergences for multiplicative intensities. 6.4. Histograms for intensity and regression functions. 6.5. Estimation of the density of duration excess. 6.6. Estimators for processes on increasing intervals. 6.7. Models with varying intensity or regression coefficients. 6.8. Progressive censoring of a random time sequence. 6.9. Exercises
- 7. Estimation in semi-parametric regression models. 7.1. Introduction. 7.2. Convergence of the estimators. 7.3. Nonparametric regression with a change of variables. 7.4. Exercises
- 8. Diffusion processes. 8.1. Introduction. 8.2. Estimation for continuous diffusions by discretization. 8.3. Estimation for continuous diffusion processes. 8.4. Estimation of discretely observed diffusions with jumps. 8.5. Continuous estimation for diffusions with jumps. 8.6. Transformations of a non-stationary Gaussian process. 8.7. Exercises
- 9. Applications to time series. 9.1. Nonparametric estimation of the mean. 9.2. Periodic models for time series. 9.3. Nonparametric estimation of the covariance function. 9.4. Nonparametric transformations for stationarity. 9.5. Change-points in time series. 9.6. Exercises.