Convergence of the conditional Kaplan-Meier estimate under strong mixing
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Publication:1346663
DOI10.1016/0378-3758(94)00084-9zbMath0813.62042OpenAlexW1998890502MaRDI QIDQ1346663
Jean-Pierre Lecoutre, Elias Ould Saïd
Publication date: 9 April 1995
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0378-3758(94)00084-9
right censoringKaplan-Meier conditional estimatestationary strong mixing processesuniform almost complete consistency
Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09)
Related Items (8)
Strong representations of the Kaplan-Meier estimator and hazard estimator with censored widely orthant dependent data ⋮ Strong Approximation of Quantile Function for Strong Mixing and Censored Processes ⋮ Empirical likelihood for conditional quantile with left-truncated and dependent data ⋮ Asymptotic properties of conditional quantile estimator for censored dependent observations ⋮ Strong representation results of the Kaplan-Meier estimator for censored negatively associated data ⋮ Asymptotic normality and Berry-Esseen results for conditional density estimator with censored and dependent data ⋮ Kaplan–Meier estimator and hazard estimator for censored negatively superadditive dependent data ⋮ A Berry-Esseen type bound in kernel density estimation for strong mixing censored samples
Cites Work
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- On almost sure convergence of conditional empirical distribution functions
- Conditional empirical processes
- Asymptotics of conditional empirical processes
- Uniform consistency of the kernel conditional Kaplan-Meier estimate
- Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator
- Nonparametric Estimation from Incomplete Observations
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