Large sample properties of kernel-type score function estimators

DOI10.1016/0378-3758(89)90097-9zbMath0693.62036OpenAlexW2013629405MaRDI QIDQ908630

Lajos Horváth, Murray D. Burke

Publication date: 1989

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(89)90097-9

zbMATH Keywords

Gaussian processFisher informationkernel estimatorempirical distributionstrong uniform consistencydifferentiable densitystrong approximation resultsKernel-type score function estimatorsL1-consistencysmoothness and boundedness conditions

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05) Strong limit theorems (60F15)

Cites Work

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• Asymptotic distributions of estimators of the derivative of a density and the score function
• Adaptive maximum likelihood estimators of a location parameter
• Strong approximations of the quantile process
• A strong law of the empirical density function
• Asymptotically efficient adaptive rank estimates in location models
• On some global measures of the deviations of density function estimates
• A nearest neighbour-estimator for the score function
• An approximation of partial sums of independent RV'-s, and the sample DF. I
• Asymptotic Properties of Gaussian Processes
• Robust Statistics

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