A note on nonparametric density estimation for dependent variables using a delta sequence

DOI10.1007/BF02480938zbMath0477.62022OpenAlexW1984413741MaRDI QIDQ1161007

Ibrahim A. Ahmad

Publication date: 1981

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02480938

zbMATH Keywords

rates of convergencestationaritymean squaremixing sequences

Mathematics Subject Classification ID

Nonparametric estimation (62G05)

Related Items (5)

On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators ⋮ Strong convergence of sums of \(\alpha \)-mixing random variables with applications to density estimation ⋮ Density estimation for Markov chains ⋮ Integrated consistency of smoothed probability density estimators for stationary sequences ⋮ Local convergency rate of MSE in density estimation using the second-order modulus of smoothness

Cites Work

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• Optimal convergence properties of variable knot, kernel, and orthogonal series methods for density estimation
• Probability density estimation using delta sequences
• A note on empirical processes of strong-mixing sequences
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• A Review of Some Non-parametric Methods of Density Estimation
• The central limit problem for mixing sequences of random variables
• Asymptotic theory of density estimation
• On the Best Obtainable Asymptotic Rates of Convergence in Estimation of a Density Function at a Point
• Nonparametric Probability Density Estimation: I. A Summary of Available Methods
• Some Limit Theorems for Stationary Processes

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