A central limit theorem for the integrated square error of the kernel density estimators with randomly censored data

DOI10.1016/0378-3758(93)90083-IzbMath0799.62052OpenAlexW1970620425WikidataQ126407814 ScholiaQ126407814MaRDI QIDQ1314484

Song Yang

Publication date: 16 February 1994

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(93)90083-i

zbMATH Keywords

accuracycentral limit theoremproduct-limit estimatorkernel estimatorKaplan-Meier estimatorgoodness of fit testsintegrated square errorrandom censorship modelHellinger distance parametric inference

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05)

Related Items (3)

Central Limit Theorem for ISE of Kernel Density Estimators in Censored Dependent Model ⋮ Asymptotic expansion for ISE of kernel density estimators under censored dependent model ⋮ Probability density estimation with data missing at random when covariables are present

Cites Work

• Central limit theorem for integrated square error of multivariate nonparametric density estimators
• Large sample behaviour of the product-limit estimator on the whole line
• Minimum Hellinger distance estimation of parameter in the random censorship model
• The power and optimal kernel of the Bickel-Rosenblatt test for goodness of fit
• Some inequalities about the Kaplan-Meier estimator
• A quadratic measure of deviation of two-dimensional density estimates and a test of independence
• On the maximal deviation of k-dimensional density estimates
• Minimum Hellinger distance estimates for parametric models
• On some global measures of the deviations of density function estimates
• Nonparametric Estimation from Incomplete Observations
• Strong approximations of some biometric estimates under random censorship
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