How to get central limit theorems for global errors of estimates.
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Publication:1775159
DOI10.1023/A:1022240820668zbMath1060.62056OpenAlexW163261952MaRDI QIDQ1775159
Publication date: 4 May 2005
Published in: Applications of Mathematics (Search for Journal in Brave)
Full work available at URL: https://eudml.org/doc/33028
Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05) Functional limit theorems; invariance principles (60F17) (L^p)-limit theorems (60F25)
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Parametric estimation and tests through divergences and the duality technique, Nonparametric estimation and inference about the overlap of two distributions
Cites Work
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- Central limit theorem for integrated square error of multivariate nonparametric density estimators
- On \(L_ p\)-norms of multivariate density estimators
- Central limit theorems for multinomial sums
- A quadratic measure of deviation of two-dimensional density estimates and a test of independence
- On the asymptotic normality of \(L_ p\)-norms of empirical functionals
- Weak convergence and empirical processes. With applications to statistics
- On some global measures of the deviations of density function estimates
- Distribution estimation consistent in total variation and in two types of information divergence
- Empirical Processes in Action: A Review
- Distribution Estimates Consistent in χ2-Divergence
- On the asymptotic normality of the L1‐ and L2‐errors in histogram density estimation
- About the asymptotic accuracy of Barron density estimates
- Asymptotic Normality ofL1-Error in Density Estimation
- On Deviations between Theoretical and Empirical Distributions