Parametrically guided nonparametric density and hazard estimation with censored data

DOI10.1016/j.csda.2015.01.009zbMath1468.62189OpenAlexW2001928467MaRDI QIDQ1660219

Anouar El Ghouch, Majda Talamakrouni, Ingrid Van Keilegom

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://alfresco.uclouvain.be/alfresco/service/guest/streamDownload/7691b492-08db-4e7f-a49d-98650e1afb48/ISBADP2014_15_vankeilegom_parametrically.pdf?guest=true

zbMATH Keywords

kernel smoothingright censoringmaximum likelihoodCox modeldensity estimationKaplan-Meier estimator

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Censored data models (62N01)

Related Items (9)

On E-Bayesian estimations for the cumulative hazard rate and mean residual life under generalized inverted exponential distribution and type-II censoring ⋮ Novel and simple non-parametric methods of estimating the joint and marginal densities ⋮ A new parametric method of estimating the joint probability density ⋮ Bias reduction by projection on parametric models in Hilbertian nonparametric regression ⋮ Hazard rate estimation for left truncated and right censored data ⋮ Special issue on advances in survival analysis ⋮ Hilbertian additive regression with parametric help ⋮ Nonparametric regression with parametric help ⋮ Comparison of parametric and semiparametric survival regression models with kernel estimation

Uses Software

• Stata
• asciker

Cites Work

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