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Varying kernel density estimation on R+.
Mnatsakanov R; Sarkisian K
Stat Probab Lett 2012 Jul; 82(7):1337-1345
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is proposed. This method is natural when estimating an unknown density function of a positive random variable. The rates of Mean Squared Error, Mean Integrated Squared Error, and the L1-consistency are investigated. Simulation studies are conducted to compare a new estimator and its modified version with traditional kernel density construction.
Statistical-analysis; Mathematical-models; Analytical-methods; Analytical-processes; Simulation-methods; Quantitative-analysis; Statistical-quality-control; Author Keywords: Varying kernel density estimator; Mean Squared Error; Mean Integrated Squared Error; delta-sequence; L1-consistency
Robert Mnatsakanov, Department of Statistics, P.O. Box 6330, West Virginia University, Morgantown, WV 26506, USA
Issue of Publication
Statistics & Probability Letters
Page last reviewed: March 25, 2022
Content source: National Institute for Occupational Safety and Health Education and Information Division