A new method for proving weak convergence results applied to nonparametric estimators in survival analysis.

DOI10.1016/S0304-4149(00)00052-1zbMath1046.62047OpenAlexW2139947932MaRDI QIDQ1879498

Jean-Yves Dauxois

Publication date: 22 September 2004

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0304-4149(00)00052-1

zbMATH Keywords

weak convergencemartingalecensored datacounting processstochastic integral

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Censored data models (62N01) Martingales with continuous parameter (60G44) Estimation in survival analysis and censored data (62N02) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (4)

Estimation in a model of competing risks possibly depending on the presence of censorships ⋮ Semiparametric inference of competing risks data with additive hazards and missing cause of failure under MCAR or MAR assumptions ⋮ Estimation de la fonction de survie sous biais de longueur pour des risques concurrents et proportionnels. (Estimation of the survival function under length-biased sampling for competing risks and proportional hazards) ⋮ Nonparametric estimation from proportional hazards competing risks data under selection bias

Cites Work

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• Weak limit theorems for stochastic integrals and stochastic differential equations
• Nonparametric inference for a family of counting processes
• A survey of product-integration with a view toward application in survival analysis
• Nonparametric Estimation from Incomplete Observations
• Statistical models based on counting processes

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