Last data update: Sep 16, 2024. (Total: 47680 publications since 2009)
Records 1-5 (of 5 Records) |
Query Trace: Mnatsakanov RM [original query] |
---|
Approximation of the ruin probability using the scaled Laplace transform inversion
Mnatsakanov RM , Sarkisian K , Hakobyan A . Appl Math Comput 2015 268 717-727 The problem of recovering the ruin probability in the classical risk model based on the scaled Laplace transform inversion is studied. It is shown how to overcome the problem of evaluating the ruin probability at large values of an initial surplus process. Comparisons of proposed approximations with the ones based on the Laplace transform inversions using a fixed Talbot algorithm as well as on the ones using the Trefethen–Weideman–Schmelzer and maximum entropy methods are presented via a simulation study. |
The Radon transform inversion using moments
Mnatsakanov RM , Li SQ . Stat Probab Lett 2013 83 (3) 936-942 The problem of recovering the multivariate probability density function f from the moments of its Radon transform Rf is studied. The approximation of the Radon transform Rf itself is obtained from the moments off. Under the mild conditions on f the uniform rates of convergence for the proposed constructions are established. (C) 2012 Elsevier B.V. All rights reserved. |
A note on recovering the distributions from exponential moments
Mnatsakanov RM , Sarkisian K . Appl Math Comput 2013 219 (16) 8730-8737 The problem of recovering a cumulative distribution function of a positive random variable via the scaled Laplace transform inversion is studied. The uniform upper bound of proposed approximation is derived. The approximation of a compound Poisson distribution as well as the estimation of a distribution function of the summands given the sample from a compound Poisson distribution are investigated. Applying the simulation study, the question of selecting the optimal scaling parameter of the proposed Laplace transform inversion is considered. The behavior of the approximants are demonstrated via plots and table. |
Moment density estimation for positive random variables
Mnatsakanov RM , Ruymgaart FH . Statistics 2012 46 (2) 215-230 An unknown moment-determinate cumulative distribution function or its density function can be recovered from corresponding moments and estimated from the empirical moments. This method of estimating an unknown density is natural in certain inverse estimation models like multiplicative censoring or biased sampling when the moments of unobserved distribution can be estimated via the transformed moments of the observed distribution. In this paper, we introduce a new nonparametric estimator of a probability density function defined on the positive real line, motivated by the above. Some fundamental properties of proposed estimator are studied. The comparison with traditional kernel density estimator is discussed. |
k-Nearest neighbor based consistent entropy estimation for hyperspherical distributions
Li SQ , Mnatsakanov RM , Andrew ME . Entropy (Basel) 2011 13 (3) 650-667 A consistent entropy estimator for hyperspherical data is proposed based on the k-nearest neighbor (knn) approach. The asymptotic unbiasedness and consistency of the estimator are proved. Moreover, cross entropy and Kullback-Leibler (KL) divergence estimators are also discussed. Simulation studies are conducted to assess the performance of the estimators for models including uniform and von Mises-Fisher distributions. The proposed knn entropy estimator is compared with the moment based counterpart via simulations. The results show that these two methods are comparable. |
- Page last reviewed:Feb 1, 2024
- Page last updated:Sep 16, 2024
- Content source:
- Powered by CDC PHGKB Infrastructure