Asymptotic normality and Berry-Esseen results for conditional density estimator with censored and dependent data
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Publication:962201
DOI10.1016/j.jmva.2010.01.002zbMath1185.62074OpenAlexW2027532275MaRDI QIDQ962201
Publication date: 6 April 2010
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2010.01.002
Density estimation (62G07) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20)
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Cites Work
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- Asymptotic properties of conditional quantile estimator for censored dependent observations
- Functional laws of the iterated logarithm for the product-limit estimator of a distribution function under random censorship or truncation
- Berry-Esseen type bounds of estimators in a semiparametric model with linear process errors
- The strong law under random censorship
- Mixing: Properties and examples
- The Kaplan-Meier estimate for dependent failure time observations
- Convergence of the conditional Kaplan-Meier estimate under strong mixing
- Strong convergence of sums of \(\alpha \)-mixing random variables with applications to density estimation
- Relative deficiency of the Kaplan-Meier estimator with respect to a smoothed estimator
- Constructing multivariate distributions with specific marginal distributions
- Nonparametric regression estimation for dependent functional data: asymptotic normality
- Maximal moment inequality for partial sums of strong mixing sequences and application
- A crossvalidation method for estimating conditional densities
- Some Limit Theorems for Random Functions. I
- Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems
- Conditions for linear processes to be strong-mixing
- On the Strong Mixing Property for Linear Sequences
- Modification of the Greenwood Formula for Correlated Response Times
- On Conditional Density Estimation
- Conditional Minimum Volume Predictive Regions for Stochastic Processes
- Nonparametric Estimation and Symmetry Tests for Conditional Density Functions
- The accuracy of the normal approximation for minimum contrast estimates
- Strong uniform consistency of nonparametric estimation of the censored conditional mode function
- Bandwidth selection for kernel conditional density estimation.
- Estimating a distribution function for censored time series data
