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Subordinate diffusions are constructed by time changing diffusion processes with an independent L\'{e}vy subordinator. This is a rich family of Markovian jump processes which exhibit a variety of jump behavior and have found many…

Statistics Theory · Mathematics 2017-06-29 Weiwei Guo , Lingfei Li

In this paper, a class of reflected generalized backward doubly stochastic differential equations (reflected GBDSDEs in short) driven by Teugels martingales associated with L\'{e}vy process and the integral with respect to an adapted…

Probability · Mathematics 2009-07-14 Auguste Aman

Consider a multivariate L\'evy-driven Ornstein-Uhlenbeck process where the stationary distribution or background driving L\'evy process is from a parametric family. We derive the likelihood function assuming that the innovation term is…

Statistics Theory · Mathematics 2021-09-01 Kevin W. Lu

We define a new family of multivariate stochastic processes over a finite time horizon that we call Generalised Liouville Processes (GLPs). GLPs are Markov processes constructed by splitting L\'evy random bridges into non-overlapping…

Probability · Mathematics 2020-11-25 Edward Hoyle , Levent Ali Mengütürk

Assume a L\'evy process $X$ on the time interval $[0,1]$ that is an $L_2$-martingale and let $Y$ be either its stochastic exponential or $X$ itself. We consider Riemann-approximations of certain stochastic integrals driven by $Y$ and relate…

Probability · Mathematics 2012-01-04 Christel Geiss , Stefan Geiss , Eija Laukkarinen

In this paper we introduce a new class of state space models based on shot-noise simulation representations of non-Gaussian L\'evy-driven linear systems, represented as stochastic differential equations. In particular a conditionally…

Probability · Mathematics 2020-01-09 Simon Godsill , Marina Riabiz , Ioannis Kontoyiannis

Large-scale Gaussian process inference has long faced practical challenges due to time and space complexity that is superlinear in dataset size. While sparse variational Gaussian process models are capable of learning from large-scale data,…

Machine Learning · Statistics 2018-01-23 Ching-An Cheng , Byron Boots

We consider iterated function systems (finite or countable), together with linear and continuous operators on Hilbert spaces, which enable us to construct Markov-type operators. Under suitable conditions, these Markov-type operators have…

Classical Analysis and ODEs · Mathematics 2017-01-30 Ion Chiţescu , Loredana Ioana , Radu Miculescu , Lucian Niţă

The inversion of a Levy measure was first introduced (under a different name) in Sato 2007. We generalize the definition and give some properties. We then use inversions to derive a relationship between weak convergence of a Levy process to…

Probability · Mathematics 2016-01-27 Michael Grabchak

Consider a Hilbert space obtained as the completion of the polynomials C[z} in m-variables for which the mnonomials are orthogonal. If the commuting weighted shifts defined by the coordinate functions are essentially normal, then the same…

Operator Algebras · Mathematics 2007-05-23 Ronald G. Douglas

Consider a sequence (Z_n,Z_n^M) of bivariate L\'evy processes, such that Z_n is a spectrally positive L\'evy process with finite variation, and Z_n^M is the counting process of marks in {0,1} carried by the jumps of Z_n. The study of these…

Probability · Mathematics 2014-03-11 Cécile Delaporte

We investigate the concept of cylindrical Wiener process subordinated to a strictly $\alpha$-stable L\'evy process, with $\alpha\in\left(0,1\right)$, in an infinite dimensional, separable Hilbert space, and consider the related stochastic…

Probability · Mathematics 2021-01-19 Alessandro Bondi

A continuous-time particle system on the real line satisfying the branching property and an exponential integrability condition is called a branching L\'evy process, and its law is characterized by a triplet $(\sigma^2,a,\Lambda)$. We…

Probability · Mathematics 2022-02-25 Bastien Mallein , Quan Shi

We study spectral-theoretic properties of non-self-adjoint operators arising in the study of one-dimensional L\'evy processes with completely monotone jumps with a one-sided barrier. With no further assumptions, we provide an integral…

Spectral Theory · Mathematics 2024-11-19 Mateusz Kwaśnicki

For linear inverse problems with Gaussian priors and Gaussian observation noise, the posterior is Gaussian, with mean and covariance determined by the conditioning formula. The covariance is the central object for uncertainty…

Statistics Theory · Mathematics 2025-08-13 Giuseppe Carere , Han Cheng Lie

This paper contains a study of multivariate second order stochastic mappings indexed by an abstract set $\Lambda$ in close connection to their operator covariance functions. The characterizations of the normal Hilbert module or of Hilbert…

Functional Analysis · Mathematics 2015-01-27 Pastorel Gaspar , Lorena Popa

Modelling spatio-temporal processes has become an important issue in current research. Since Gaussian processes are essentially determined by their second order structure, broad classes of covariance functions are of interest. Here, a new…

Statistics Theory · Mathematics 2011-02-28 Martin Schlather

These lectures notes aim at introducing L\'{e}vy processes in an informal and intuitive way, accessible to non-specialists in the field. In the first part, we focus on the theory of L\'{e}vy processes. We analyze a `toy' example of a…

Pricing of Securities · Quantitative Finance 2008-12-02 Antonis Papapantoleon

Let $X=\{X(t)\}_{t\geq0}$ be an operator semistable L\'evy process in $\rd$ with exponent $E$, where $E$ is an invertible linear operator on $\rd$ and $X$ is semi-selfsimilar with respect to $E$. By refining arguments given in Meerschaert…

Probability · Mathematics 2014-09-11 Peter Kern , Lina Wedrich

We derive characteristic function identities for conditional distributions of an r-trimmed Levy process given its r largest jumps up to a designated time t. Assuming the underlying Levy process is in the domain of attraction of a stable…

Probability · Mathematics 2018-09-06 Yuguang F. Ipsen , Peter Kevei , Ross A. Maller
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