Nonparametric estimation of conditional probability densities and expectations of stationary processes: Strong consistency and rates

DOI10.1016/0304-4149(89)90056-2zbMath0692.62034OpenAlexW2083363652MaRDI QIDQ583762

Elias Masry

Publication date: 1989

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4149(89)90056-2

zbMATH Keywords

conditional expectationsstrong consistencymixing conditionsalmost sure convergence ratesconditional probability densitiesconditioning on past behaviourjoint probability densityreal- valued stationary processRecursive kernel estimators

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09)

Related Items

Recursive estimation for stochastic damping hamiltonian systems, Prediction in invertible linear processes, Efficient prediction for linear and nonlinear autoregressive models, Recursive estimation of the transition distribution function of a Markov process: Asymptotic normality, Strong consistency and rates for recursive nonparametric conditional probability density estimates under \((\alpha{}, \beta{})\)-mixing conditions, Prediction in moving average processes, Conditional Density Estimation in the Single Functional Index Model for α-Mixing Functional Data, Nonparametric estimation of the ratios of derivatives of a multivariate distribution density from dependent observations, Nonparametric estimation for stationary and strongly mixing processes on Riemannian manifolds, Multivariate probability density estimation for associated processes: Strong consistency and rates.

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