Stochastic Convexity of the Poisson Mixture Model

This paper is devoted to the study of the compound Poisson mixture model in an actuarial framework. Using the s-convex stochastic orderings and stochastic s-convexity, several problems involving an unknown mixing parameter with given moments are examined; namely, the specification of the number of support points in a finite mixture model, and the derivation of extremal mixture distributions. The theory is enhanced with the derivation of theoretical and numerical bounds on several quantities of actuarial interest.     

Minimizing Increasing Star-shaped Functions Based on Abstract Convexity

We study a broad class of increasing non-convex functions whose level sets are star shaped with respect to infinity. We show that these functions (we call them ISSI functions) are abstract convex with respect to the set of min-type functions and exploit this fact for their minimization. An algorithm is proposed for solving global optimization problems with an ISSI objective function and its numerical performance is discussed.     

Stochastic Ordering of Multivariate Normal Distributions

We show an interesting identity for Ef(Y) – Ef(X), where X, Yare normally distributed random vectors and f is a function fulfilling some weak regularity condition. This identity will be used for a unified derivation of sufficient conditions for stochastic ordering results of multivariate normal distributions, some well known ones as well as some new ones. Moreover, we will show that many of these conditions are also necessary. As examples we will consider the usual stochastic order, convex order, upper orthant order, supermodular order and directionally convex order.     

Statistical Korovkin Theory for Multivariate Stochastic Processes

In this article, we study Korovkin-type approximation theorems for multivariate stochastic processes via the concept of A-statistical convergence. A non-trivial example expressing the importance of our results is also presented.     

Multivariate Stochastic Orders Induced by Case-Control Sampling

It is shown that retrospective sampling induces stochastic order relations in case-control studies. More specifically if the regression function is increasing and the covariates are positively dependent, then the covariates for cases are larger, with respect to some multivariate stochastic order, than the covariates of the controls. Strong dependence concepts yield strong multivariate stochastic orders. Conversely, different multivariate stochastic orders imply different monotonicity properties on the regression function. The results carry over to marginal models, transformed models and to problems involving confounders. The results set forth a new theoretical foundation for the analysis of case-control studies.     

A Stochastic Directional Convexity Result and Its Application in Comparison of Queues

Second order properties of queues are important in design and analysis of service systems. In this paper we show that the blocking probability of M/M/C/N queue is increasing directionally convex in (λ,?μ), where λ is arrival rate and μ is service rate. To illustrate the usefulness of this result we consider a heterogeneous queueing system with non-stationary arrival and service processes. The arrival and service rates alternate between two levels (λ₁,μ₁) and (λ₂,μ₂), spending an exponentially distributed amount of time with rate cα _i in level i, i=1,2. When the system is in state i, the arrival rate is λ _i and the service rate is μ _i . Applying the increasing directional convexity result we show that the blocking probability is decreasing in c, extending a result of Fond and Ross [7] for the case C=N=1.

     

Stochastic Programming with Multivariate Second Order Stochastic Dominance Constraints with Applications in Portfolio Optimization

In this paper we study optimization problems with multivariate stochastic dominance constraints where the underlying functions are not necessarily linear. These problems are important in multicriterion decision making, since each component of vectors can be interpreted as the uncertain outcome of a given criterion. We propose a penalization scheme for the multivariate second order stochastic dominance constraints. We solve the penalized problem by the level function methods, and a modified cutting plane method and compare them to the cutting surface method proposed in the literature. The proposed numerical schemes are applied to a generic budget allocation problem and a real world portfolio optimization problem.     

Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit nonidentifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate dataset illustrates the superior performance of the new approach for real-world data. Supplementary materials for this article are available online.     

广义次序统计量间隔的多维随机排序

本文研究了附加于广义次序统计量底分布以及参数的条件, 使得人们在多维似然比序和多维通常随机序意义下对广义次序统计量的间隔向量进行比较, 同时也给出了文中主要结果的应用.     

随机利率下的增额寿险模型

以即时给付的增额寿险为研究对象,在保证利率恒正的情况下,考虑到不同性质的信息对利率的影响,对利率的随机性采用带Poisson跳的反射Brown运动建模,给出了一次缴清净保费、净均衡年保费和连续缴费方式下S时刻责任准备金的一般表达式.     

PRHRģ�ʹ���ͳ��������ͨ��Ԫ�����

Let,,,
be all independent PRHR variables. Firstly, we show thatimplies. Secondly, we consider the comparison of
convolutions of independent heterogeneous PRHR variables with respect to the usual stochastic
ordering. Suppose and, we prove that implies,
for all. The results established here strengthen some of the results known in
the literature.     

Discrete Convexity

In the paper, we explain what subsets of the lattice ℤ ⁿ and what functions on the lattice ℤ ⁿ could be called convex. The basis of our theory is the following three main postulates of classical convex analysis: concave functions are closed under sums; they are also closed under convolutions; and the superdifferential of a concave function is nonempty at each point of the domain. Interesting (and even dual) classes of discrete concave functions arise if we require either the existence of superdifferentials and closeness under convolutions or the existence of superdifferentials and closeness under sums. The corresponding classes of convex sets are obtained as the affinity domains of such discretely concave functions. The classes of the first type are closed under (Minkowski) sums, and the classes of the second type are closed under intersections. In both classes, the separation theorem holds true. Unimodular sets play an important role in the classification of such classes. The so-called polymatroidal discretely concave functions, most interesting for applications, are related to the unimodular system . Such functions naturally appear in mathematical economics, in Gelfand-Tzetlin patterns, play an important role for solution of the Horn problem, for describing submodule invariants over discrete valuation rings, and so on. Bibliography: 6 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 312, 2004, pp. 86–93.     

The Compact Law of the Iterated Logarithm for Multivariate Stochastic Approximation Algorithms

Abstract

We consider a sequence (Z _n ) _n≥1 defined by a general multivariate stochastic approximation algorithm and assume that (Z _n ) converges to a solution z* almost surely. We establish the compact law of the iterated logarithm for Z _n by proving that, with probability one, the limit set of the sequence (Z _n  ? z*) suitably normalized is an ellipsoid. We also give the law of the iterated logarithm for the l ^p norms, p ∈ [1, ∞], of (Z _n  ? z*).     

Partial Convexity

We study OC-convexity, which is defined by the intersection of conic semispaces of partial convexity. We investigate an optimization problem for OC-convex sets and prove a Krein--Milman type theorem for OC-convexity. The relationship between OC-convex and functionally convex sets is studied. Topological and numerical aspects, as well as separability properties are described. An upper estimate for the Carathéodory number for OC-convexity is found. On the other hand, it happens that the Helly and the Radon number for OC-convexity are infinite. We prove that the OC-convex hull of any finite set of points is the union of finitely many polyhedra.     

一类随机利率下的增额寿险

寿险中的利率随机问题，是近来保险精算研究的热点和重点问题之一。本以即时给付的一类增额寿险为对象，对随机利率采用Gauss过程建模，研究给付现值及其各阶矩。     

Depth-Optimized Convexity Cuts

This paper presents a general, self-contained treatment of convexity or intersection cuts. It describes two equivalent ways of generating a cut—via a convex set or a concave function—and a partial-order notion of cut strength. We then characterize the structure of the sets and functions that generate cuts that are strongest with respect to the partial order. Next, we specialize this analytical framework to the case of mixed-integer linear programming (MIP). For this case, we formulate two kinds of the deepest cut generation problem, via sets or via functions, and subsequently consider some special cases which are amenable to efficient computation. We conclude with computational tests of one of these procedures on a large set of MIPLIB problems.