Asymptotic error distribution for the Euler scheme with locally Lipschitz coefficients

In traditional works on numerical schemes for solving stochastic differential equations (SDEs), the globally Lipschitz assumption is often assumed to ensure different types of convergence. In practice, this is often too strong a condition. Brownian motion driven SDEs used in applications sometimes have coefficients which are only Lipschitz on compact sets, but the paths of the SDE solutions can be arbitrarily large. In this paper, we prove convergence in probability and a weak convergence result under a less restrictive assumption, that is, locally Lipschitz and with no finite time explosion. We prove if a numerical scheme converges in probability uniformly on any compact time set (UCP) with a certain rate under a global Lipschitz condition, then the UCP with the same rate holds when a globally Lipschitz condition is replaced with a locally Lipschitz plus no finite explosion condition. For the Euler scheme, weak convergence of the error process is also established. The main contribution of this paper is the proof of $\sqrt{n}$ weak convergence of the normalized error process and the limit process is also provided. We further study the boundedness of the second moments of the weak limit process and its running supremum under both global Lipschitz and locally Lipschitz conditions.     

Stochastic systems with memory and jumps

Stochastic systems with memory naturally appear in life science, economy, and finance. We take the modelling point of view of stochastic functional delay equations and we study these structures when the driving noises admit jumps. Our results concern existence and uniqueness of strong solutions, estimates for the moments and the fundamental tools of calculus, such as the Itô formula. We study the robustness of the solution to the change of noises. Specifically, we consider the noises with infinite activity jumps versus an adequately corrected Gaussian noise. The study is presented in two different frameworks: we work with random variables in infinite dimensions, where the values are considered either in an appropriate $L^p$-type space or in the space of càdlàg paths. The choice of the value space is crucial from the modelling point of view, as the different settings allow for the treatment of different models of memory or delay. Our techniques involve tools of infinite dimensional calculus and the stochastic calculus via regularisation.     

Force microscopy analysis using chemometric tools

In this paper we report the first application of multivariate data analysis techniques to force spectrometry measurement sets to enable the physicochemical assignment of spatially ordered multi-component systems. Principal component analysis (PCA) and hierarchical clustering techniques were used to reveal hidden chemical information within force-distance curves generated by high spatial resolution force microscopy. Two experimental samples were analyzed: (i) a two-component system of cytochrome c proteins on a mica surface, and (ii) a three-component system of avidin protein islands positioned on a gold and glass surface. PCA and hierarchical clustering techniques were used to discriminate the different components of the two-component system, whereas hierarchical clustering was found to be superior for the three-component system. Results were in good agreement with the topography and prior knowledge of the surface patterns. This research represents a formative step towards the combination of force spectrometry with chemometric tools for the high resolution physicochemical investigation of complex biochemical systems.     

Stochastic limit-average games are in EXPTIME

The value of a finite-state two-player zero-sum stochastic game with limit-average payoff can be approximated to within in time exponential in a polynomial in the size of the game times polynomial in logarithmic in , for all .     

The SC property of an expected residual function arising from stochastic complementarity problems

The stochastic nonlinear complementarity problem has been recently reformulated as an expected residual minimization problem which minimizes an expected residual function defined by an NCP function. In this work, we show that the expected residual function defined by the Fischer–Burmeister function is an function.     

Characterizations of classes of risk measures by dispersive orders

In this paper, a class C₁ of risk measures, which generalizes the class of risk measures for the right-tail deviation suggested by Wang [Wang, S., 1998. An actuarial index of the right-tail risk. North Amer. Actuarial J. 2, 88–101], is characterized in terms of dispersive order. If dispersive order does not hold, unanimous comparisons are still possible by restricting our attention to a subclass C₂C₁ and then the criterion is the excess-wealth order. Sufficient conditions for stochastic equivalence of excess-wealth ordered random variables are derived in terms of some particular measures of C₂.     

An M/G/1 system with startup server and J additional options for service

We consider an M^[x]/G/1 queueing system with a startup time, where all arriving customers demand first the essential service and some of them may further demand one of other optional services: Type 1, Type 2, … , and Type J service. The service times of the essential service and of the Type i  (i=1,2,…,J)$(i=1\text{,}2\text{,}\dots \text{,}J)$ service are assumed to be random variables with arbitrary distributions. The server is turned off each time when the system is empty. As soon as a customer or a batch of customers arrives, the server immediately performs a startup which is needed before starting each busy period. We derive the steady-state results, including system size distribution at a random epoch and at a departure epoch, the distributions of idle and busy periods, and waiting time distribution in the queue. Some special cases are also presented.