Parameter estimation for linear fractional stable noise process
Over the past few years, scaling phenomena involving self-similarity and heavy-tailed distributions have attracted the interest of various researchers in telecommunications and networks. In this paper, we study the linear fractional stable noise (LFSN) which exhibits both long-range dependence and heavy tails property. LFSN can be represented as a linear process with weight coefficients and a-stable random variables. The coefficients of the linear process are determined by a kernel function and depend on five parameters. This paper focuses on estimating two unknown parameters a and b. Based on minimizing square errors, several methods for estimating these two parameters are presented. Detailed tables and graphs have been included in extensive simulations which show the methods are good estimates.
parameter estimation heavy-tailed linear fractional stable noise (lfsn) long-range dependence alpha-stable non-gaussian random process