In the last decade the subordinated processes have become popular and found many practical applications. Therefore in this paper we examine two processes related to time-changed (subordinated) classical Brownian motion with drift (called arithmetic Brownian motion). The first one, so called normal tempered stable, is related to the tempered stable subordinator, while the second one- to the inverse tempered stable process. We compare the main properties (such as probability density functions, Laplace transforms, ensemble averaged mean squared displacements) of such two subordinated processes and propose the parameters ’ estimation procedures. Moreover we calibrate the analyzed systems to real data related to indoor air quality
AbstractThe class of stochastic processes is characterized which, as multiplicative noise with large...
It has been observed that data often appears to be well approximated by infinite variance stable dis...
Stable Lévy processes and related stochastic processes play an important role in stochastic modellin...
AbstractA tempered stable Lévy process combines both the α-stable and Gaussian trends. In a short ti...
In the classical analysis many models used to real data description are based on the standard Browni...
This brief is concerned with tempered stable distributions and their associated Levy processes. It i...
The first-exit time process of an inverse Gaussian Levy process is considered. The one-dimensional d...
In this paper we study the fractional Brownian motion (FBM) time changed by the inverse Gaussian (IG...
87 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.In this thesis, a class of pro...
In an early article on near-unit root autoregression, Ahtola and Tiao (1984) studied the behavior of...
Fractional derivatives and integrals are convolutions with a power law. Including an exponential ter...
AbstractFractional tempered stable motion (fTSm) is defined and studied. FTSm has the same covarianc...
We consider a standard Brownian motion whose drift alternates randomly between a positive and a nega...
This work defines two classes of processes, that we term tempered fractional multistable motion and ...
Abstract Tempered fractional stable motion adds an exponential tempering to the power-law kernel in ...
AbstractThe class of stochastic processes is characterized which, as multiplicative noise with large...
It has been observed that data often appears to be well approximated by infinite variance stable dis...
Stable Lévy processes and related stochastic processes play an important role in stochastic modellin...
AbstractA tempered stable Lévy process combines both the α-stable and Gaussian trends. In a short ti...
In the classical analysis many models used to real data description are based on the standard Browni...
This brief is concerned with tempered stable distributions and their associated Levy processes. It i...
The first-exit time process of an inverse Gaussian Levy process is considered. The one-dimensional d...
In this paper we study the fractional Brownian motion (FBM) time changed by the inverse Gaussian (IG...
87 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.In this thesis, a class of pro...
In an early article on near-unit root autoregression, Ahtola and Tiao (1984) studied the behavior of...
Fractional derivatives and integrals are convolutions with a power law. Including an exponential ter...
AbstractFractional tempered stable motion (fTSm) is defined and studied. FTSm has the same covarianc...
We consider a standard Brownian motion whose drift alternates randomly between a positive and a nega...
This work defines two classes of processes, that we term tempered fractional multistable motion and ...
Abstract Tempered fractional stable motion adds an exponential tempering to the power-law kernel in ...
AbstractThe class of stochastic processes is characterized which, as multiplicative noise with large...
It has been observed that data often appears to be well approximated by infinite variance stable dis...
Stable Lévy processes and related stochastic processes play an important role in stochastic modellin...