利率为马氏链的离散时间风险模型的破产概率(英文)

本文考虑了带随机利率的离散时间风险模型.在假设利率为马氏链条件下,得到了有限时间和最终破产概率所满足的递推积分方程,以及最终破产概率的Lundberg不等式.     

相关随机利率下的双二项模型的破产概率

研究带有相关随机利率的双二项风险模型,得到了破产概率的积分表达式,并利用鞅分析的方法得到了破产概率的经典Lundberg上界,另外给出了一个破产概率的比经典Lundberg上界更精确的上界.     

一类推广的双险种复合Poisson风险模型的破产概率

本文研究了一类索赔计数过程相关的双险种Poisson风险模型.利用模型转化首先将该复杂模型转化为经典的风险模型,获得该模型破产概率所满足的积分方程,Lundberg上界表达式,及Cramér-Lundberg渐近估计式.当个体索赔具有指数分布时,推得了破产概率所满足的方程,并给出了具体的数值计算的实例.     

一类推广负风险和模型的破产概率

本文研究了带干扰的两险种负风险和模型的破产问题.利用无穷小方法,给出了该风险模型破产概率所满足的微分-积分方程,并推导出破产概率满足的Lundberg型不等式.最后指出了当索赔服从负指数分布时破产概率的上界,推广了经典风险模型的结果.     

保费收取过程是随机过程的双险种风险模型

研究保费收取过程是一个随机过程的双险种风险模型,得出了Lundberg上界、最终破产概率、不破产所满足的微积分方程、索赔服从指数分布的不破产概率、有限时间不破产所满足的微积分方程.     

索赔次数为复合Poisson-Geometric过程的常利率风险模型的罚金函数

讨论了常利率下索赔次数为复合Poisson-Geometric过程的风险模型的罚金函数,得到了罚金函数的期望所满足的积分方程,并由所得到的积分方程推出了破产概率所满足的积分方程,初始盈余为0时,得到了罚金函数的期望及破产概率的精确解.     

考虑Markov保费和利率的离散时间风险模型的破产概率

研究一类离散时间风险模型的破产概率.在保费收入和利率同时为离散时间Markov链,索赔额为独立情形下,利用更新迭代方法得到最终时间破产概率的Lundberg型上界.     

常利率下的Cox模型的破产概率

讨论了常利率下的Cox模型的破产概率 ,分别得到了条件破产概率和最终破产概率所满足的积分方程 .     

一类推广的复合Poisson-Geometric相依风险模型的破产概率

研究了一类推广的复合Poisson—Geometric风险相依模型．利用盈余过程的鞅性,得到了破产概率公式以及破产概率所满足的积分方程和Cramer—Lundberg逼近．最后给出了索赔额服从指数分布时Cramer-Lundberg逼近的精确表达式．     

索赔次数为复合Poisson-Geometric过程的双险种风险模型

对索赔次数为复合Poisson-Geometric过程的双险种风险模型进行研究,给出了生存概率所满足的积分方程、指数分布下的具体表达式及有限时间内的积分—微分方程,并利用鞅方法得到了最终破产概率的Lundberg不等式和一般公式.     

响应变量删失情况下线性模型的FIC模型选择和模型平均

本文给出了响应变量随机右删失情况下线性模型的FIC (focused information criterion) 模型选择方法和光滑FIC 模型平均估计方法, 证明了兴趣参数的FIC 模型选择估计和光滑FIC 模型平均估计的渐近正态性, 通过随机模拟研究了估计的有限样本性质, 模拟结果显示, 从均方误差和一定置信水平置信区间的经验覆盖概率看, 兴趣参数的光滑FIC 模型平均估计均优于FIC, AIC (Akaikeinformation criterion) 和BIC (Bayesian information citerion) 等模型选择估计; 而FIC 模型选择估计与AIC 和BIC 等模型选择估计相比, 也表现出了一定的优越性. 通过分析原发性胆汁性肝硬化数据集, 说明了本文方法在实际问题中的应用.     

Four-vertex model

An exactly solvable four-vertex model on a square grid with different boundary conditions is considered. The application of the Algebraic Bethe Ansatz method allows us to calculate the partition function of the model. For fixed boundary conditions, we establish a relation between the scalar product of state vectors with the generating function of column-and row-strict boxed plane partitions. A tiling model on a periodic grid is discussed. Bibliography: 33 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 347, 2007, pp. 34–55.     

Reduced-order model predictive control of a fish schooling model

We study the problem of model predictive control (MPC) for the fish schooling model proposed by Gautrais et al. (2008). The high nonlinearity of the model attributed to its attraction/alignment/repulsion law suggests the need to use MPC for controlling the fish schooling’s motion. However, for large schools, the hybrid nature of the law can make it numerically demanding to perform finite-horizon optimizations in MPC. Therefore, this paper proposes reducing the fish schooling model for numerically efficient MPC; the reduction is based on using the weighted average of the directions of individual fish in the school. We analytically show how using the normalized eigenvector centrality of the alignment-interaction network can yield a better reduction by comparing reduction errors. We confirm this finding on the weight and numerical efficiency of the MPC with the reduced-order model by numerical simulations. The proposed reduction allows us to control a school with up to 500 individuals. Further, we confirm that reduction with the normalized eigenvector centrality allows us to improve the control accuracy by factor of five when compared to that using constant weights.     

A generalized linear production model: A unifying model

We introduce a generalized linear production model whose attractive feature being that the resources held by any subset of producersS is not restricted to be the vector sum of the resources held by the members ofS. We provide sufficient conditions for the non-emptiness of the core of the associated generalized linear production game, and show that if the core of the game is not empty then a solution in it can be produced from a dual optimal solution to the associated linear programming problem. Our generalized linear production model is a proper generalization of the linear production model introduced by Owen, and it can be used to analyze cooperative games which cannot be studied in the ordinary linear production model framework. We use the generalized model to show that the cooperative game induced by a network optimization problem in which players are the nodes of the network has a non-empty core. We further employ our model to prove the non-emptiness of the core of two other classes of cooperative games, which were not previously studied in the literature, and we also use our generalized model to provide an alternative proof for the non-emptiness of the core of the class of minimum cost spanning tree games. Thus, it appears that the generalized linear production model is a unifying model which can be used to explain the non-emptiness of the core of cooperative games generated by various, seemingly different, optimization models.This research was partially done while the author was visiting the Graduate School of Business Administration at Tel-Aviv University. The research was partially supported by Natural Sciences and Engineering Research Council Canada Grant A4181 and by SSHRC leave fellowship 451-83-0030.Dedicated to George B. Dantzig.     

一个基于Sidrauski宏观经济动力学模型的分析

Abstract. This paper formulates a kind of dynamical macro-economic model based onSidrauski‘s work,then presents the sufficient and necessary conditions of the stability of modelat equilibrium states ,and shows some results for special production functions.     

Periodic real-time scheduling: from deadline-based model to latency-based model

This paper presents a connection between two real-time models: a deadline-based model and a latency-based model. The importance of the latency-based model is proved through a result showing that two deadlines, instead of a latency constraint, over-constrain the real-time applications. Moreover, we give a deadline-marking algorithm based on the relation between deadlines and latency constraints. This algorithm provides non-preemptive feasible schedules for systems with precedence constraints and deadlines, or more complex systems with deadlines and latencies. This is the first step toward non-preemptive schedulability for distributed architectures (without over-constraining the system) like, for example, the automotive applications using protocols such as Controller Area Network (CAN). These results were obtained while the first author was at INRIA Rocquencourt.     

A replacement model for an additive damage model with restoration

A system existing in a random environment receives shocks at random points of time. Each shock causes a random amount of damage which accumulates over time. A breakdown can occur only upon the occurrence of a shock according to a known failure probability function. Upon failure the system is replaced by a new identical one with a given cost. When the system is replaced before failure, a smaller cost is incurred. Thus, there is an incentive to attempt to replace the system before failure. The damage process is controlled by means of a maintenance policy which causes the accumulated damage to decrease at a known restoration rate. We introduce sufficient conditions under which an optimal replacement policy which minimizes the total expected discounted cost is a control limit policy. The relationship between the undiscounted case and the discounted case is examined. Finally, an example is given illustrating computational procedures.     

Double-markov risk model

Given a new Double-Markov risk model DM=(μ,Q,ν,H;Y,Z) and Double-Markov risk process U={U(t),t≥ 0}. The ruin or survival problem is addressed. Equations which the survival probability satisfied and the formulas of calculating survival probability are obtained. Recursion formulas of calculating the survival probability and analytic expression of recursion items are obtained. The conclusions are expressed by Q matrix for a Markov chain and transition probabilities for another Markov Chain.     

A nucleation-and-growth model

Summary. We consider the following simple nucleation-and-growth model. On the lattice ℤ ^d , starting with all sites unoccupied, a site becomes occupied at rate e ^−ℬΓ if it has no occupied neighbors, at rate ɛ= e ^−βγ if it has 1 occupied neighbor, and at rate 1 if it has 2 or more occupied neighbors. Occupied sites remain occupied forever. The parameters Γ≧γ are fixed, and we are interested in the behavior of the system as β→∞. We show that the relaxation time of this system scales as e ^βκc , where κ _c = max {γ,( Γ + γ)/(d+1)}. Received: 20 February 1996 / In revised form: 15 June 1996     

Gaussian model selection

Our purpose in this paper is to provide a general approach to model selection via penalization for Gaussian regression and to develop our point of view about this subject. The advantage and importance of model selection come from the fact that it provides a suitable approach to many different types of problems, starting from model selection per se (among a family of parametric models, which one is more suitable for the data at hand), which includes for instance variable selection in regression models, to nonparametric estimation, for which it provides a very powerful tool that allows adaptation under quite general circumstances. Our approach to model selection also provides a natural connection between the parametric and nonparametric points of view and copes naturally with the fact that a model is not necessarily true. The method is based on the penalization of a least squares criterion which can be viewed as a generalization of Mallows’C _p . A large part of our efforts will be put on choosing properly the list of models and the penalty function for various estimation problems like classical variable selection or adaptive estimation for various types of l _p -bodies. Received February 1, 1999 / final version received January 10, 2001?Published online April 3, 2001