Plug-in bandwidth selection in kernel hazard estimation from dependent data
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Publication:1020678
DOI10.1016/j.csda.2006.10.010zbMath1445.62074OpenAlexW2076973672MaRDI QIDQ1020678
Publication date: 2 June 2009
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2006.10.010
kernel estimationplug-indistribution functiondensity functionbandwidth selectionhazardstrong mixing processes
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Cites Work
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- Asymptotic normality of the kernel estimate under dependence conditions: Application to hazard rate
- Data-driven bandwidth choice for density estimation based on dependent data
- Quadratic errors for nonparametric estimates under dependence
- Comparison of two bandwidth selectors with dependent errors
- Mixing: Properties and examples
- Nonparametric estimation of density derivatives of dependent data
- Optimal smooth hazard estimates
- Convergence rate for cross-validatory bandwidth in kernel hazard estimation from dependent samples
- Bandwidth selection for kernel distribution function estimation
- Bandwidth selection in nonparametric density estimation under dependence: a simulation study
- Bandwidth choice for nonparametric hazard rate estimation
- Smoothing parameter selection for smooth distribution functions
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- Estimation of the failure rate-a survey of nonparametric methods Part I: Non-Bayesian Methods
- On the Choice of the Bandwidth in Kernel Nonparametric Regression
- Bootstrap Selection of the Smoothing Parameter in Nonparametric Hazard Rate Estimation
- Multistage plug—in bandwidth selection for kernel distribution function estimates
- Nonparametric estimation of the hazard function under dependence conditions
- Hazard analysis. I
- Some Limit Theorems for Stationary Processes