Markov process functionals in finance and insurance

The Markov property of Markov process functionals which are frequently used in economy, finance, engineering and statistic analysis is studied. The conditions to judge Markov property of some important Markov process functionals are presented, the following conclusions are obtained： the multidimensional process with independent increments is a multidimensional Markov process; the functional in the form of path integral of process with independent increments is a Markov process; the surplus process with the doubly stochastic Poisson process is a vector Markov process. The conditions for linear transformation of vector Markov process being still a Markov process are given.

DUAL RANDOM MODEL OF INCREASING ANNUITY

The dual random models about the life insurance and social pension insurance have received considerable attention in the recent articles on actuarial theory and applications. This paper discusses a general kind of increasing annuity based on its force of interest accumulation function as a general random process. The dual random model of the present value of the benefits of the increasing annuity has been set, and their moments have been calculated under certain conditions.

Moments and limiting distribution of a portfolio of whole life annuity policies

A dual random model of a portfolio of variable amount whole life annuity is set with the mth moment of the present value of benefits, and the respective expressions of the moments under the assumption that the force of interest accumulation function is Wiener process or Ornstein-Uhlenbeck process. Furthermore, the limiting distribution of average cost of this portfolio is discussed with the expression of the limiting distribution under the assumption that the force of interest accumulation is an independent increment process.

MULTI-DIMENSIONAL MARKOV CHAIN–BASED ANALYSIS OF CONFLICT PROBABILITY FOR SPECTRUM RESOURCE SHARING

In this paper, we consider the optimal problem of channels sharing with het-erogeneous traffic （real-time service and non-real-time service） to reduce the data conflict probability of users. Moreover, a multi-dimensional Markov chain model is developed to analyze the performance of the proposed scheme. Meanwhile, performance metrics are derived. Numerical results show that the proposed scheme can effectively reduce the forced termination probability, blocking probability and spectrum utilization.

Continuous-Time Independent Edge-Markovian Random Graph Process

In this paper, the continuous-time independent edge-Markovian random graph process model is constructed. The authors also define the interval isolated nodes of the random graph process, study the distribution sequence of the number of isolated nodes and the probability of having no isolated nodes when the initial distribution of the random graph process is stationary distribution, derive the lower limit of the probability in which two arbitrary nodes are connected and the random graph is also connected, and prove that the random graph is almost everywhere connected when the number of nodes is sufficiently large.

A STRASSEN LAW OF THE ITERATED LOGARITHM FOR PROCESSES WITH INDEPENDENT INCREMENT

Let X={X(t),t 0} be a process with independent increments (PII)such that E=0, D X(t)E 2<∞, lim t→∞D X(t)t=1, and there exists a majoring measure G for the jump △X of X . Under these assumptions, using rather a direct method, a Strassen's law of the iterated logarithm (Strassen LIL) is established. As some special cases,the Strassen LIL for homogeneous PII and for partial sum process of i.i.d.random variables are comprised.

A LAW OF THE ITERATED LOGARITHM FOR PROCESSES WITH INDEPENDENT INCREMENTS

By using the Ito's calculus, a law of the iterated logarithm is established for the processes with independent increments (PⅡ). Let X = {Xt, t ≥ 0} be a PII with Ext=0,V(t)=Ext2<∞and limt∞V(t)=∞ If one of the following conditions is satisfied,(2) Suppose the Levy's measure of X may be written as v(dt,ds) = Ft(dx) dV(t) and there is a σ-finite measure G such tnat ,

An Assignment Method for IPUs in Distributed Systems

In a distributed system, one of the most important things is to establish an assignment method for distributing tasks. It is assumed that a dis tributed system does not have a central administrator, all independent processing units in this system want to cooperate for the best results, but they cannot know the conditions of one another. So in order to undertake the tasks in admirable pro portions, they have to adjust their undertaking tasks only by self-learning. In this paper, the performance of this system is analyzed by Markov chains, and a robust method of self-learning for independent processing units in this kind of systems is presented. This method can lead the tasks of the system to be distributed very well among all the independent processing units, and can also be used to solve the general assignment problem.

Comparison of two reliability models for performance degradation analysis

In order to investigate the general reliability assessment methods based on performance degradation data,two commonly used stochastic process approaches,bilinear process method and random-effect model were studied.Analyzing procedure and effectiveness of these two methodologies were studied and compared.Meanwhile,the two approaches were illustrated through practical applications.The residual plots and the 10th percentile curves of the two methods were presented to demonstrate the comparative results.The randomeffect model yielded more volatile residuals and a lower and unsafe 10th percentile curve.Consequently the bilinear process model can be concluded to derive more reasonable results due to its one-stage estimation property.

Random Stabilization of Sampled-data Control Systems with Nonuniform Sampling

For a sampled-data control system with nonuniform sampling, the sampling interval sequence, which is continuously distributed in a given interval, is described as a multiple independent and identically distributed (i.i.d.) process. With this process, the closed-loop system is transformed into an asynchronous dynamical impulsive model with input delays. Sufficient conditions for the closed-loop mean-square exponential stability are presented in terms of linear matrix inequalities (LMIs), in which the relation between the nonuniform sampling and the mean-square exponential stability of the closed-loop system is explicitly established. Based on the stability conditions, the controller design method is given, which is further formulated as a convex optimization problem with LMI constraints. Numerical examples and experiment results are given to show the effectiveness and the advantages of the theoretical results.