Central limit theorems for \(L_ p\) distances of kernel estimators of densities under random censorship
From MaRDI portal
Publication:1184207
DOI10.1214/AOS/1176348372zbMath0747.60022OpenAlexW2016152260MaRDI QIDQ1184207
Lajos Horváth, Edit Gombay, Miklós Csörgő
Publication date: 28 June 1992
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aos/1176348372
central limit theoremskernel density functiontests of goodness of fitKaplan-Meier product-limit estimator
Nonparametric hypothesis testing (62G10) Gaussian processes (60G15) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05)
Related Items (9)
The LIL for the Bickel-Rosenblatt test statistic ⋮ Central Limit Theorem for ISE of Kernel Density Estimators in Censored Dependent Model ⋮ A bound on the \(\mathcal{L}_ 1\)-error of a nonparametric density estimator with censored data ⋮ The Lp consistency of error density estimator in censored linear regression ⋮ Asymptotic of theLr-norm of density estimators in the autoregressive time series ⋮ A pointwise Bayes-type estimator of the survival probability with censored data ⋮ Functional asymptotic normality of the \(L_{2}\)-deviation of the kernel density estimation indexed by classes of weight functions ⋮ Some inequalities for the kernel density estimator under random censorship∗ ⋮ Exact asymptotic \(\mathcal L_1\)-error of a kernel density estimator under censored data.
This page was built for publication: Central limit theorems for \(L_ p\) distances of kernel estimators of densities under random censorship
