A note on asymptotic expansions for sums over a weakly dependent random field with application to the Poisson and Strauss processes

Previous results on Edgeworth expansions for sums over a random field are extended to the case where the strong mixing coefficient depends not only on the distance between two sets of random variables, but also on the size of the two sets. The results are applied to the Poisson and the Strauss point processes, giving rise also to local limit results.     

A phase field model in electrowetting and related phenomena

The term electrowetting is commonly used for some techniques to change the shape and wetting behaviour of liquid droplets by the application of electric fields and charges. We developand analyze a model for electrowetting that combines the Navier-Stokes system for fluid flow, a phase-field model of Cahn-Hilliard type for the movement of the interface, a charge transport equation, and the potential equation of electrostatics. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On a compound Poisson risk model with delayed claims and random incomes

The main focus of this paper is to analyze the Gerber-Shiu penalty function of a compound Poisson risk model with delayed claims and random incomes. It is assumed that every main claim will produce a by-claim which can be delayed with a certain probability. We derive the integral equation satisfied by the Gerber-Shiu penalty function. Given that the premium size is exponentially distributed, the explicit expression for the Laplace transform of the Gerber-Shiu penalty function is derived. Finally, when the premium sizes have rational Laplace transforms, we also obtain the Laplace transform of the Gerber-Shiu penalty function.     

Amenable ergodic group actions and an application to Poisson boundaries of random walks

We introduce and study the class of amenable ergodic group actions which occupy a position in ergodic theory parallel to that of amenable groups in group theory. We apply this notion to questions about skew products, the range (i.e., Poincaré flow) of a cocycle, and to Poisson boundaries.     

Truncated random variables with application to random surfaces

There has been considerable interest in obtaining discrete results for random surfaces. Standard results have been published in journals of physics or engineering which have emphasised the applications. This paper gives a detailed background of the mathematical methods needed so that the central connection, namely truncated random variables, between these standard results can be understood. Distributions of discrete peak measures are obtained from the distributions of discrete profile measures of a random Gaussian surface by applying results for the distributions of truncated random variables. This enable the moments to be obtained from known results for the truncated distributions.     

A preferential attachment model with Poisson growth for scale-free networks

We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barabási and Albert where a network is generated iteratively from a small seed network; at each step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike the Barabási–Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula for the probability of a network under our model.     

A random tree model associated with random graphs

Grow a tree on n vertices by starting with no edges and successively adding an edge chosen uniformly from the set of possible edges whose addition would not create a cycle. This process is closely related to the classical random graph process. We describe the asymptotic structure of the tree, as seen locally from a given vertex. In particular, we give an explicit expression for the asymptotic degree distribution. Our results an be applied to study the random minimum-weight spanning tree question, when the edge-weight distribution is allowed to vary almost arbitrarily with n.     

Optimal filters for a hidden Markov random field model

A Markov random field (MRF) is a useful technical tool for modeling dynamics systems exhibiting some type of spatio-temporal variability. In this paper, we propose optimal filters for the states of a partially observed temporal Markov random field. We also discuss parameters estimation. This generalizes an earlier work by Elliott and Aggoun [1].     

A likelihood ratio formula for random fields with application to physical geodesy

A formula for the likelihood ratio applicable to estimation and inference problems arising in random field data models in physical geodesy is derived based on multiparameter white noise theory.Research supported in part under grant no. 78-3550, Applied Mathematics Division, AFOSR, United States Air Force.     

A one-dimensional Poisson growth model with non-overlapping intervals

Suppose given a realization of a Poisson process on the line: call the points ‘germs’ because at a given instant ‘grains’ start growing around every germ, stopping for any particular grain when it touches another grain. When all growth stops a fraction e⁻¹ of the line remains uncovered. Let n germs be thrown uniformly and independently onto the circumference of a circle, and let grains grow under a similar protocol. Then the expected fraction of the circle remaining uncovered is the nth partial sum of the usual series for e⁻¹. These results, which sharpen inequalities obtained earlier, have one-sided analogues: the grains on the positive axis alone do not cover the origin with probability e^−1/2, and the conditional probability that the origin is uncovered by these positive grains, given that the germs n and n+1 coincide, is the nth partial sum of the series for e^−1/2. Despite the close similarity of these results to the rencontre, or matching, problem, we have no inclusion–exclusion derivation of them. We give explicitly the distributions for the length of a contiguous block of grains and the number of grains in such a block, and for the length of a grain. The points of the line not covered by any grain constitute a Kingman-type regenerative phenomenon for which the associated p-function p(t) gives the conditional probability that a point at distance t from an uncovered point is also uncovered. These functions enable us to identify a continuous-time Markov chain on the integers for which p(t) is a diagonal transition probability.     

Exact asymptotics in a mean field model with random potential

Summary For a mean field operator with a random potential, asymptotic properties of the eigenvalues and eigenfunctions are studied and applied to investigate the longerm behavior of the solutions of a corresponding large system of differential equations. The total mass of the system is approximately concentrated in the record point of the random potential (complete localization). A more detailed inspection of the peaks shows that there is a phase transition: Only in the case of a moderate increase of time relatively to the growth of the space size the model behaves similarly to the system without diffusion. But also in the non-moderate case the asymptotic height of peaks can exactly be described.     

A Markov modulated Poisson model for software reliability

In this paper, we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures. The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.     

Likelihood inference for the destructive exponentially weighted Poisson cure rate model with Weibull lifetime and an application to melanoma data

In this paper, we develop the steps of the expectation maximization algorithm (EM algorithm) for the determination of the maximum likelihood estimates (MLEs) of the parameters of the destructive exponentially weighted Poisson cure rate model in which the lifetimes are assumed to be Weibull. This model is more flexible than the promotion time cure rate model as it provides an interesting and realistic interpretation of the biological mechanism of the occurrence of an event of interest by including a destructive process of the initial number of causes in a competitive scenario. The standard errors of the MLEs are obtained by inverting the observed information matrix. An extensive Monte Carlo simulation study is carried out to evaluate the performance of the developed method of estimation. Finally, a known melanoma data are analyzed to illustrate the method of inference developed here. With these data, a comparison is also made with the scenario when the destructive mechanism is not included in the analysis.     

A random point field related to Bose-Einstein condensation

The random point fields which describe the position distributions of the systems of ideal boson gas in states of Bose-Einstein condensations are obtained through the thermodynamic limits. The resulting point fields are given by convolutions of two kinds of independent point fields: the so-called boson processes whose generating functionals are represented by the inverses of the Fredholm determinants for operators related to the heat operator and the point fields whose generating functionals are represented by the resolvents of the operators. The construction of the latter point fields in an abstract formulation is also given.     

A statistical model for random rotations

This paper studies the properties of the Cayley distributions, a new family of models for random p×p rotations. This class of distributions is related to the Cayley transform that maps a p(p-1)/2×1 vector s into SO(p), the space of p×p rotation matrices. First an expression for the uniform measure on SO(p) is derived using the Cayley transform, then the Cayley density for random rotations is investigated. A closed-form expression is derived for its normalizing constant, a simple simulation algorithm is proposed, and moments are derived. The efficiencies of moment estimators of the parameters of the new model are also calculated. A Monte Carlo investigation of tests and of confidence regions for the parameters of the new density is briefly summarized. A numerical example is presented.     

A hybrid maintenance model with imperfect inspection for a system with deterioration and Poisson failure

A new maintenance model for a system with both deterioration and Poisson failures is proposed. In this model, at any time-instant G _S and when the system is operating, one of the following decisions may be taken: (1) stop the system to perform a scheduled minimal maintenance; (2) stop the system to perform an inspection; and (3) no action and allow the system to go on with its operation. Following an inspection, based on the deterioration condition of the system, one of the following decisions may be taken: (a) if the system is in a ‘good’ condition, no maintenance action is taken and a number of periodic minimal maintenance activities are scheduled, starting T1 later; (b) if the system is in an ‘intermediate’ condition, a minimal maintenance is performed and an inspection is scheduled for T2 later (T2 < T1); and (c) if the system is in a ‘bad’ condition, a major maintenance is performed and a number of periodic minimal maintenances are scheduled, starting T1 later. In addition, a deterioration failure is restored by a major repair and a Poisson failure is restored by a minimal repair. Generalised stochastic Petri nets are used to represent and analyse the model, which represents a ‘composite’ maintenance strategy. Based on maximisation of the throughput of the system the benefit of this model compared to (1) an equivalent periodic inspection model and (2) an equivalent planned scheduled maintenance model, is demonstrated. This study presents a new hybrid model with a general framework for incorporating various types of maintenance policies. Also by incorporation of a number of features, this model will be more applicable to real world technical systems (complex systems), although it can be applied to individual components that are part of a complex system.