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Consider a single-server queue with a Poisson arrival process and exponential processing times in which each customer independently reneges after an exponentially distributed amount of time. We establ...
We consider a binary unsupervised classi cation problem where each observation is associated with an unobserved label that we want to retrieve. More precisely, we assume that there are two groups of...
We consider questions of characterizing a stochastic process X = (X,,t 2 0) by the properties of the first two conditional moments. Our first result is a new version of the classical P. Levy charac...
The problem of constructing impulsive rebalancing of portfolios, introduced by Pliska and Suzuki, is solved for models with general Markovian prices. Existence of the optimal strategy iu established...
We show that if a strictly positive joint probability distribution for a set of binary variables factors according to a tree, then vertex separation represents all and only the independence relation...
Moments of the Discounted Dividends in a Threshold-Type Markovian Risk Process。
Random Walk in Dynamic Markovian Random Environment.
This is an expository review paper illustrating the “martin- gale method” for proving many-server heavy-traffic stochastic-process limits for queueing models, supporting diffusion-process approximatio...
Let $(xi_k, kge 0)$ be a Markov chain on ${-1,+1}$ with $xi_0=1$ and transition probabilities $P(xi_{k+1}=1| xi_k=1)=a>b=P(xi_{k+1}=-1| xi_k=-1)$. Set $X_0=0$, $X_n=xi_1+cdots +xi_n$ and $M_n=max_{0le...
A recent theorem by M. Manstavicius (2004) provided a link between a certain function of transition probabilities of a strong Markov process and the boundedness of the p-variation of its trajectories....
A recent theorem by M. Manstavicius (2004) provided a link between a certain function of transition probabilities of a strong Markov process and the boundedness of the p-variation of its trajectories....
Let X be a stochastic process obeying a stochastic differential equation of the form dXt = b(Xt, θ)dt + dYt, where Y is an adapted driving process possibly depending on X’s past history, and θ ∈ Θ ...

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