Computationally Efficient Bootstrap Expressions for Bandwidth Selection in Nonparametric Curve Estimation
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Computationally Efficient Bootstrap Expressions for Bandwidth Selection in Nonparametric Curve EstimationFecha
2018-09-17Cita bibliográfica
Barbeito, I.; Cao, R. Computationally Efficient Bootstrap Expressions for Bandwidth Selection in Nonparametric Curve Estimation. Proceedings 2018, 2, 1164.
Resumen
[Abstract] Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed bootstrap). In these contexts, four new bandwidth parameter selectors are proposed based on closed bootstrap expressions of the MISE of the kernel density estimator (case 1) and two approximations of the kernel hazard rate estimation (case 2). These expressions turn out to be very useful since Monte Carlo approximation is no longer needed. Finally, these smoothing parameter selectors are empirically compared with the already existing ones via a simulation study.
Palabras clave
Hazard rate
Kernel method
Mean integrated squared error
Moving blocks bootstrap
Smooth bootstrap
Smoothing parameter
Stationary bootstrap
Stationary processes
Kernel method
Mean integrated squared error
Moving blocks bootstrap
Smooth bootstrap
Smoothing parameter
Stationary bootstrap
Stationary processes
Descripción
Trátase dun resumo estendido da ponencia
Versión del editor
Derechos
Atribución 3.0 España
ISSN
2504-3900