On the rate of convergence of recursive kernel estimates of probability densities

DOI10.2307/3314937zbMath0669.62016OpenAlexW2002400042MaRDI QIDQ3822986

No author found.

Publication date: 1988

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3314937

zbMATH Keywords

density estimationstrictly stationary processesasymptotic independence-uncorrelatedness conditionsasymptotic variance- covariancestationary triangular arrays of random variables

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Markov processes: estimation; hidden Markov models (62M05)

Related Items (1)

Non-parametric estimation of the conditional mode

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic normality of some kernel-type estimators of probability density
• On smoothed probability density estimation for stationary processes
• A note on empirical processes of strong-mixing sequences
• Nonparametric estimation in Markov processes
• A unified theory of regularly varying sequences
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• Probability density estimation from sampled data
• Sequential and recursive estimators of the probability density
• Recursive probability density estimation for weakly dependent stationary processes
• Central Limit Theorems for dependent variables. I
• On a Class of Estimates of the Probability Density Function and mode based on a Random Number of Observations
• Recursive Estimation in Diffusion Model
• A Note on Permanents

This page was built for publication: On the rate of convergence of recursive kernel estimates of probability densities