Differential equations of stochastic processes which have derivative in quadratic mean
Karel Koštál (1967)
Czechoslovak Mathematical Journal
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Displaying similar documents to “Nonparametric density estimators based on nonstationary absolutely regular random sequences.”
Differential equations of stochastic processes which have derivative in quadratic mean
Dependent Lindeberg central limit theorem and some applications
Jean-Marc Bardet, Paul Doukhan, Gabriel Lang, Nicolas Ragache (2008)
ESAIM: Probability and Statistics
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In this paper, a very useful lemma (in two versions) is proved: it simplifies notably the essential step to establish a Lindeberg central limit theorem for dependent processes. Then, applying this lemma to weakly dependent processes introduced in Doukhan and Louhichi (1999), a new central limit theorem is obtained for sample mean or kernel density estimator. Moreover, by using the subsampling, extensions under weaker assumptions of these central limit theorems are provided. All the...
Some extensions of an inequality of Vapnik and Chervonenkis.
Panchenko, Dmitriy (2002)
Electronic Communications in Probability [electronic only]
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Limiting behavior of the perturbed empirical distribution functions evaluated at -statistics for strongly mixing sequences of random variables.
Sun, Shan, Chiang, Ching-Yuan (1997)
Journal of Applied Mathematics and Stochastic Analysis
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On the constant in the nonuniform version of the Berry-Esseen theorem.
Neammanee, K. (2005)
International Journal of Mathematics and Mathematical Sciences
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A tail inequality for suprema of unbounded empirical processes with applications to Markov chains.
Adamczak, Radoslaw (2008)
Electronic Journal of Probability [electronic only]
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