We propose the model, which allows us to approximate fractional Levy noise and fractional Levy motion. Our model is based (i) on the Gnedenko limit theorem for an attraction basin of stable probability law, and (ii) on regarding fractional noise as the result of fractional integration/differentiation of a white Levy noise. We investigate self-affine properties of the approximation and conclude ...

If the rules that regulate a market allow speculation then prices in that market become volatile. It is possible to model the fluctuations in these prices as random variables. The empirical distributions of these random variables are unusual; they are distinctly non-Gaussian. They belong to a class of distributions called Levy stable distributions and have paradoxical properties. The processes ...

We established a sparse estimation method for the generalized exponential marked Hawkes process by penalized to ordinary (P–O) estimator. Furthermore, we evaluated probability of correct variable selection. In course this, framework likelihood analysis and P–O when there might be nuisance parameters, true value parameter at boundary space. Finally, numerical simulations are given several import...

Levy and Levy (Management Science 2002) present data that, according to their claims, violate prospect theory. They suggest that prospect theory’s hypothesis of an S-shaped value function, concave for gains and convex for losses, is incorrect. However, all the data of Levy and Levy are perfectly consistent with the predictions of prospect theory, as can be verified by simply applying prospect t...

15. Olive, C., Geroch, M. E. & Levy, H. R. ( 197 1 ) J. Biol. Chem. 246,2047-2057 Levy, H. R. & Daouk, G. H. (1979) J. Biol. Chem. 254, 4843-4847 Grove, T. H., Ishaque, A. & Levy, H. R. ( I 976) Arch. Biochem. Biophys. 177,307-316 Levy, H. R., Christoff, M., Ingulli, J. & Ho, E. M. L. ( 1 983) Arch. Biochem. Biophys. 222,473-488 Levy, H. R.. Daouk, G. H. & Katopes, M. A. (1979) Arch. Riochem. B...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...