Convergence rate for cross-validatory bandwidth in kernel hazard estimation from dependent samples
From MaRDI portal
Publication:1600739
DOI10.1016/S0378-3758(01)00245-2zbMath0988.62021MaRDI QIDQ1600739
Philippe Vieu, Graciela Estevez-Perez, Alejandro Quintela-del-Río
Publication date: 16 June 2002
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
rate of convergencebandwidth selectionstrongly mixing processeskernel hazard estimationmoments inequalitypenalized cross-validation
Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Inference from stochastic processes (62M99)
Related Items
Local linear estimate of the point at high risk: Spatial functional data case ⋮ A Review and Some New Proposals for Bandwidth Selection in Nonparametric Density Estimation for Dependent Data ⋮ Estimation and simulation of conditional hazard function in the quasi-associated framework when the observations are linked via a functional single-index structure ⋮ Consistency rates and asymptotic normality of the high risk conditional for functional data ⋮ Consistency rates and asymptotic normality of the high risk conditional for functional data ⋮ Nonparametric estimation of the maximum hazard under dependence conditions ⋮ Plug-in bandwidth selection in kernel hazard estimation from dependent data ⋮ Nonparametric estimation under long memory dependence ⋮ Nonparametric Estimation for the Hazard Function
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Smoothing parameter selection in hazard estimation
- Bandwidth choice for nonparametric regression
- Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation
- On the amount of noise inherent in bandwidth selection for a kernel density estimator
- Quadratic errors for nonparametric estimates under dependence
- Nonparametric kernel regression estimation near endpoints.
- On the least squares cross-validation bandwidth in hazard rate estimation
- A comparative study of several smoothing methods in density estimation
- A study on bandwidth selection in density estimation under dependence
- Optimal smooth hazard estimates
- Moment bounds for mixing random variables useful in nonparametric function estimation
- Asymptotic behaviors of some measures of accuracy in nonparametric curve estimation with dependent observations
- Bandwidth choice for nonparametric hazard rate estimation
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- Smooth optimum kernel estimators near endpoints
- Recursive probability density estimation for weakly dependent stationary processes
- Kernel Regression Estimation Using Repeated Measurements Data
- How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
- Empirical distribution function for mixing random variables. application in nonparametric hazard estimation
- Bootstrap Selection of the Smoothing Parameter in Nonparametric Hazard Rate Estimation
- Hazard Rate Estimation under Random Censoring with Varying Kernels and Bandwidths
- Nonparametric estimation of the hazard function under dependence conditions
- Hazard analysis. I
- Convergence of Distributions Generated by Stationary Stochastic Processes
