Document Type


Publication Date



Eberly College of Arts and Sciences




New nonparametric procedure for estimating the probability density function of a positive random variable is suggested. Asymptotic expressions of the bias term and Mean Squared Error are derived. By means of graphical illustrations and evaluating the Average of L2-errors we conducted comparisons of the finite sample performance of proposed estimate with the one based on kernel density method.

Source Citation

Elmagbri, F., & Mnatsakanov, R. M. (2018). Nonparametric density estimation based on the scaled Laplace transform inversion. Transactions of A. Razmadze Mathematical Institute, 172(3), 440–447.


⃝c 2018 Ivane Javakhishvili Tbilisi State University. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (



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