Asymptotic normality of some kernel-type estimators of probability density

DOI10.1016/0167-7152(83)90048-2zbMath0521.62032OpenAlexW2043463105MaRDI QIDQ1055112

Richard C. jun. Bradley

Publication date: 1983

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(83)90048-2

zbMATH Keywords

asymptotic normalitycentral limit theoremsmixing conditionkernel-type estimatorsmaximal correlationdensity estimators

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05) Sequential estimation (62L12)

Related Items (15)

New robust confidence intervals for the mean under dependence ⋮ Three theorems on \(\rho^*\)-mixing random fields ⋮ OnL₁-consistency of kernel-type density estimator for stationary markov processes ⋮ Integrated consistency of smoothed probability density estimators for stationary sequences ⋮ Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence ⋮ Density estimation for point processes ⋮ On the rate of convergence of recursive kernel estimates of probability densities ⋮ Sums of weakly dependent random variables ⋮ Estimating a density under pointwise constraints on the derivatives ⋮ Asymptotic normality for density kernel estimators in discrete and continuous time ⋮ Asymptotic normality of nonparametric estimators under \(\alpha\)-mixing condition ⋮ LocalL-estimators for nonparametric regression under dependence ⋮ Estimators in step regression models ⋮ On kernel estimators of density for reversible Markov chains ⋮ Asymptotic normality of the kernel estimate under dependence conditions: Application to hazard rate

Cites Work

• Central limit theorems under weak dependence
• Invariance principles for mixing sequences of random variables
• Nonparametric estimation in Markov processes
• Remarks on Some Nonparametric Estimates of a Density Function
• A Sufficient Condition for Linear Growth of Variances in a Stationary Random Sequence
• A Note on the Central Limit Theorems for Dependent Random Variables
• Asymptotically optimal discriminant functions for pattern classification
• On Estimation of a Probability Density Function and Mode
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