The L/sub 1/ and L/sub 2/ strong consistency of recursive kernel density estimation from dependent samples

DOI10.1109/18.54901zbMath0706.62043OpenAlexW1993027999MaRDI QIDQ3486671

Elias Masry, László Györfi

Publication date: 1990

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/18.54901

zbMATH Keywords

strong laws of large numbersmixing processesergodic stationary processesHilbert space-valued mixingalesSharp rates of almost sure convergencestrong consistency of recursive kernel density estimators

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

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Minimum Hellinger distance estimation for discretely observed stochastic processes using recursive kernel density estimator ⋮ On bandwidth choice for density estimation with dependent data ⋮ Nonparametric inference for ergodic, stationary time series ⋮ Multivariate probability density estimation by wavelet methods: Strong consistency and rates for stationary time series ⋮ OnL₁-consistency of kernel-type density estimator for stationary markov processes ⋮ Iterative kernel density estimation from noisy-dependent observations ⋮ Some automated methods of smoothing time-dependent data ⋮ Nonparametric estimation of a regression function and its derivatives under an ergodic hypothesis ⋮ On a class of recursive estimators for spatially dependent observations ⋮ Strongly consistent density estimation of the regression residual ⋮ Unnamed Item ⋮ Strongly Consistent Detection for Nonparametric Hypotheses ⋮ Kernel density estimation from ergodic sample is not universally consistent ⋮ Robust kernel estimators for additive models with dependent observations ⋮ On density estimation from ergodic processes ⋮ Nearest neighbor regression estimation for null-recurrent Markov time series ⋮ Multivariate probability density estimation for associated processes: Strong consistency and rates.

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