Ricardas Zitikis - Academia.edu (original) (raw)
Papers by Ricardas Zitikis
IEEE Transactions on Reliability
Ratios involving incomplete gamma functions and their monotonicity properties play important role... more Ratios involving incomplete gamma functions and their monotonicity properties play important roles in financial risk analysis. We derive desired monotonicity properties either using Pinelis' Calculus Rules or applying probabilistic techniques. As a consequence, we ob- tain several inequalities involving conditional expectations that have been of interest in actuarial science.
Bulletin of the American Mathematical Society
We consider weak convergence of empirical measures generated by stationary random process X pertu... more We consider weak convergence of empirical measures generated by stationary random process X perturbed by deterministic noise N. We assume that the noise N has asymptotic distribution. In particular, we demonstrate that if the process X is ergodic, or satisfies some mixing assumptions, then the influence of deterministic noise N on X is the same as it would be if N were stochastic. Such results are of importance when investigating fluctuations and convex rearrangements of stochastic processes.
Journal of Probability and Statistics, 2009
Journal of Probability and Statistics, 2010
IEEE Transactions on Reliability
Ratios involving incomplete gamma functions and their monotonicity properties play important role... more Ratios involving incomplete gamma functions and their monotonicity properties play important roles in financial risk analysis. We derive desired monotonicity properties either using Pinelis' Calculus Rules or applying probabilistic techniques. As a consequence, we ob- tain several inequalities involving conditional expectations that have been of interest in actuarial science.
Bulletin of the American Mathematical Society
We consider weak convergence of empirical measures generated by stationary random process X pertu... more We consider weak convergence of empirical measures generated by stationary random process X perturbed by deterministic noise N. We assume that the noise N has asymptotic distribution. In particular, we demonstrate that if the process X is ergodic, or satisfies some mixing assumptions, then the influence of deterministic noise N on X is the same as it would be if N were stochastic. Such results are of importance when investigating fluctuations and convex rearrangements of stochastic processes.
Journal of Probability and Statistics, 2009
Journal of Probability and Statistics, 2010