sequential estimation of the hgarginal density function for a strongly mixing process

DOI10.1080/07474949808836411zbMath0914.62058OpenAlexW2094880747MaRDI QIDQ4225633

Toshio Honda

Publication date: 21 June 1999

Published in: Sequential Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474949808836411

zbMATH Keywords

asymptotic efficiencydensity estimationkernel estimatormean integrated squared errorstrongly mixing processfully sequential procedure

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: estimation (62M09) Sequential estimation (62L12)

Related Items (1)

Non-Parametric Sequential Estimation of a Regression Function Based on Dependent Observations

Cites Work

• Unnamed Item
• Using stopping rules to bound the mean integrated squared error in density estimation
• Strong convergence of sums of \(\alpha \)-mixing random variables with applications to density estimation
• Asymptotic behaviors of some measures of accuracy in nonparametric curve estimation with dependent observations
• On the Integral Mean Square Error of Some Nonparametric Estimates for the Density Function
• Convergence rate of the normal approximation for sequential estimators of a probability density and its derivatives
• BANDWIDTH SELECTION IN KERNEL SMOOTHING OF TIME SERIES
• Fixed width confidence bands for density functions

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