Stochastic modeling was used to predict nitrogen dioxide and fine particles [particles collected with an upper 50% cut point of 2.5 microm aerodynamic diameter (PM2.5)] levels at 1,669 addresses of the participants of two ongoing birth cohort studies conducted in Munich, Germany. Alternatively, the Gaussian multisource dispersion model IMMIS(net/em) was used to estimate the annual mean values f...

We consider an alternative approach to the use of nonlinear stochastic Markov processes (which have a Fokker-Planck or Forward Kolmogorov representation for density) in modeling uncertainty in populations. These alternate formulations, which involve imposing probabilistic structures on a family of deterministic dynamical systems, are shown to yield pointwise equivalent population densities. Mor...

the hub location decision is a long term investment and any changes in it take considerable time and money. in real situations, some parameters are uncertain hence, deterministic models cannot be more efficient. the ability of two-stage stochastic programming is to make a long-term decision by considering effects of it in short term decisions simultaneously. in the two-stage stochastic programm...

Stochastic dominance constraints allow a decision-maker to manage risk in an optimization setting by requiring their decision to yield a random outcome which stochastically dominates a reference random outcome. We present new integer and linear programming formulations for optimization under first and second-order stochastic dominance constraints, respectively. These formulations are more compa...

We study the pattern statistics representing the number of occurrences of a given string in a word of length n generated at random by rational stochastic models, defined by means of weighted finite automata. We get asymptotic estimations for the mean value and the variance of these statistics under the hypothesis that the matrix of all transition weights is primitive. Our results extend previou...

Estimating the number of eigenvalues located in a given interval of a large sparse Hermitian matrix is an important problem in certain applications and it is a prerequisite of eigensolvers based on a divide-and-conquer paradigm. Often an exact count is not necessary and methods based on stochastic estimates can be utilized to yield rough approximations. This paper examines a number of technique...

We consider a manufacturer’s stochastic production/inventory problem under periodic review and present concepts for safety stock determination to cope with uncertainties that are caused by stochastic demand and different types of yield randomness. Order releases follow a linear control rule. Taking manufacturing lead times into account it turns out that safety stocks have to be considered that ...

In this paper a simulation approach for defaultable yield curves is developed within the Heath et al. (1992) framework. The default event is modelled using the Cox process where the stochastic intensity represents the credit spread. The forward credit spread volatility function is affected by the entire credit spread term structure. The paper provides the defaultable bond and credit default swa...

In this paper, we consider a stochastic programming approach to multi-stage posttax portfolio optimization. Asset performance information is speci ed as a scenario tree generated by two alternative methods based on simulation and optimization. We assume three tax wrappers involving the same instruments for an eÆcient investment strategy and determine optimal allocations to di erent instruments ...