Conditional Viability for Impulse Differential Games

We introduce in this paper the concept of “impulse evolutionary game”. Examples of evolutionary games are usual differential games, differentiable games with history (path-dependent differential games), mutational differential games, etc. Impulse evolutionary systems and games cover in particular “hybrid systems” as well as “qualitative systems”. The conditional viability kernel of a constrained set (with a target) is the set of initial states such that for all strategies (regarded as continuous feedbacks) played by the second player, there exists a strategy of the first player such that the associated run starting from this initial state satisfies the constraints until it hits the target. This paper characterizes the concept of conditional viability kernel for “qualitative games” and of conditional valuation function for “qualitative games” maximinimizing an intertemporal criterion. The theorems obtained so far about viability/capturability issues for evolutionary systems, conditional viability for differential games and about impulse and hybrid systems are used to provide characterizations of conditional viability under impulse evolutionary games.     

Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models

This paper deals with a scalar response conditioned by a functional random variable. The main goal is to estimate nonparametrically some characteristics of this conditional distribution. Kernel type estimators for the conditional cumulative distribution function and the successive derivatives of the conditional density are introduced. Asymptotic properties are stated for each of these estimates, and they are applied to the estimations of the conditional mode and conditional quantiles. Our asymptotic results highlightes the importance of the concentration properties on small balls of the probability measure of the underlying functional variable. So, a special section is devoted to show how our results behave in several situations when the functional variable is a continuous time process, with special attention to diffusion processes and Gaussian processes. Even if the main purpose of our paper is theoretical, an application to some chemiometrical data set coming from food industry is presented in a short final section. This example illustrates the easy implementation of our method as well as its good behaviour for finite sample sizes.     

带有相依重尾冲击的Poisson噪音过程尾的一致渐近性质

本文考虑了带有某种相依重尾冲击的Poisson噪音过程尾的一致渐近性质.当冲击是二元上尾渐近独立的非负随机变量具有长尾和控制变化尾分布且噪音函数具有正的上下界时,得到了过程尾概率的一致渐近公式.进而,当冲击具有连续的一致变化尾分布时,去除了噪音函数具有正的下界的限制.对于噪音函数不一定具有正的上界的情形,当冲击具有两两负象限相依结构时,也得到了一致渐近性结果.     

Functional limit theorems for a new class of non-stationary shot noise processes

We study a class of non-stationary shot noise processes which have a general arrival process of noises with non-stationary arrival rate and a general shot shape function. Given the arrival times, the shot noises are conditionally independent and each shot noise has a general (multivariate) cumulative distribution function (c.d.f.) depending on its arrival time. We prove a functional weak law of large numbers and a functional central limit theorem for this new class of non-stationary shot noise processes in an asymptotic regime with a high intensity of shot noises, under some mild regularity conditions on the shot shape function and the conditional (multivariate) c.d.f. We discuss the applications to a simple multiplicative model (which includes a class of non-stationary compound processes and applies to insurance risk theory and physics) and the queueing and work-input processes in an associated non-stationary infinite-server queueing system. To prove the weak convergence, we show new maximal inequalities and a new criterion of existence of a stochastic process in the space $\mathbb{D}$ given its consistent finite dimensional distributions, which involve a finite set function with the superadditive property.     

Black-box Identification of Transfer Functions: Asymptotic Results for Increasing Model Order and Data Records

The problem of estimating the transfer function of a linearstochastic system is considered. The transfer function is parametrizedas a black box and no given order is chosen a priori. This meansthat the model order may increase to infinity when the numberof observed data tends to infinity. The consistency and convergenceproperties of the resulting transfer-function estimates areinvestigated. In an earlier paper results were given for models,parametrized as finite impulse responses. In this article theseresults are extended to more general parametrizations and togeneral noise models. A main result is that the variance ofthe transfer-function estimate at a certain frequency is asymptoticallygiven by the noise-to-signal ratio at that frequency, multipliedby the ratio of the model order to the number of data points.     

Fixed mesh approximation of ordinary differential equations with impulses

When trains of impulse controls are present on the right-hand side of a system of ordinary differential equations, the solution is no longer smooth and contains jumps which can accumulate at several points in the time interval. In technological and physical systems the sum of the absolute value of all the impulses is finite and hence the total variation of the solution is finite. So the solution at best belongs to the space BV of vector functions with bounded variation. Unless variable node methods are used, the loss of smoothness of the solution would a priori make higher-order methods over a fixed mesh inactractive. Indeed in general the order of -convergence is and the nodal rate is . However in the linear case -convergence rate remains but the nodal rate can go up to by using one-step or multistep scheme with a nodal rate up to when the solution belongs to . Proofs are given of error estimates and several numerical experiments confirm the optimality of the estimates. Received March 15, 1996 / Revised version received January 3, 1997     

带有不完全信息随机截尾试验下混合泊松分布参数的点估计

首先,通过添加数据,得到了带有不完全信息随机截尾试验下混合泊松分布的完全数据似然函数,然后分别利用EM算法和MCMC方法,对参数进行了估计,最后进行了随机模拟试验.结果表明参数点估计的精度比较高,     

Estimation of IIR Systems with Binary-Valued Observations*

Estimation and control problems with binary-valued observations exist widely in practical systems. However, most of the related works are devoted to finite impulse response (FIR for short) systems, and the theoretical problem of infinite impulse response (IIR for short) systems has been less explored. To study the estimation problems of IIR systems with binary-valued observations, the authors introduce a projected recursive estimation algorithm and analyse its global convergence properties, by using the stochastic Lyapunov function methods and the limit theory on double array martingales. It is shown that the estimation algorithm has similar convergence results as those for FIR systems under a weakest possible non-persistent excitation condition. Moreover, the upper bound for the accumulated regret of adaptive prediction is also established without resorting to any excitation condition.     

基于条件分布函数的高维回归模型的变量选择方法

高维回归分析的变量选择问题是目前统计学研究的一个热点和难点问题.提出了一个基于条件分布函数的相关性度量准则,并在此基础上提出三种变量选择方法.与现有的方法相比,提出的方法不依赖于统计模型,可以适用于线性模型和非参数可加模型.数值模拟结果表明,即使协变量之间存在一定的相关性,方法也有较为满意的表现.     

Conditional laplace transforms for bayesian nonparametric inference in reliabity theory

In order to apply nonparametric meyhods to reliability problems, it is desibrable to have available priors over a broad class of survival distributions.In the paper, this is achieved by taking the failure rate function to be the sum oof a nonnegative stochastic process with increasing sample patjhs and a process with decreasing sample paths. This approach produces a prior which chooses an absolutely survival distribution that can have an IFR, DFR, or U-shapped failure rate. Posterior Laplace transforms of the failure rate are obtained based on survival data allows censoring. Bayes estimates of the failure rate as well as the lifetime distribution are then calculated from these posterior Laplace transforms. This approach is also applied to a competing risks model and the proportional hazards model of Cox.     

Moderate deviations for a risk model based on the customer-arrival process

In this paper, we investigate the moderate deviations for a customer-arrival-based insurance risk model, in which customer’s actual claim sizes are described as independent and identically distributed heavy-tailed random variables multiplying a shot function, and the model can be treated as a Poisson shot noise process.     

An extension to Norton's equivalent

The aggregation method for queueing networks known as the Norton's equivalent is interpreted as a conditional estimate of the intensities of associated point processes. For multi-class Markovian queueing networks, it is shown that a first-order equivalent system of an isolated station can be obtained via the conditional estimates of intensities of the arrival and departure processes to and from that station. Based on these conditional estimates, separation results for optimal flow control problems in queueing networks can be obtained. Several examples which illustrate these concepts are given. The results obtained here generalize those which require the product form networks.     

Estimate of the concentration function for a class of additive functions

We show how methods for estimating the concentration function of the sum of independent random variables can be used to obtain estimates of the concentration function for the values of additive functions under suitable conditions. Previously obtained estimates of the concentration function are consequences of the estimate obtained in the present paper for functions from of the class under consideration.     

Positive periodic solutions for a neutral impulsive delayed Lotka–Volterra competition system with the effect of toxic substance

Based on the coincidence degree theory and its related continuation theorem as well as some a priori estimates, we explore the existence of periodic solutions with strictly positive components of a neutral delayed Lotka–Volterra competitive system with impulse. Easily verifiable sufficient criteria are established for the existence of positive periodic solution for the neutral impulsive system. When the results reduce to neutral system without impulse, they generalize the results in Yang and Cao [Positive periodic solutions of neutral Lotka–Volterra system with periodic delays, Appl. Math. Comput. 149 (2004) 661–687].     

Saddlepoint approximations to P-values for comparison of density estimates

Summary  This article is concerned with computing approximate p-values for the maximum of the absolute difference between kernel density estimates. The approximations are based on treating the process of local extrema of the differences as a nonhomogeneous Poisson Process and estimating the corresponding local intensity function. The process of local extrema is characterized by the intensity function, which determines the rate of local extrema above a given threshold. A key idea of this article is to provide methods for more accurate estimation of the intensity function by using saddlepoint approximations for the joint density of the difference between kernel density estimates and using the first and second derivative of the difference. In this article, saddlepoint approximations are compared to gaussian approximations. Simulation results from saddlepoint approximations show consistently better agreement between empirical p-value and predetermined value with various bandwidths of kernel density estimates.     

比例再保险模型的脉冲与正则 最优控制策略

首次在比例再保险模型中引入脉冲控制过程,为获得最优回报函数,不但给出了该函数所应满足的充分性定理,而且得出了该函数的具体解析式及相应的最优脉冲与正则控制策略.     

Gradient methods with regularization for constrained optimization problems and their complexity estimates

We suggest simple implementable modifications of conditional gradient and gradient projection methods for smooth convex optimization problems in Hilbert spaces. Usually, the custom methods attain only weak convergence. We prove strong convergence of the new versions and establish their complexity estimates, which appear similar to the convergence rate of the weakly convergent versions. Preliminary results of computational tests confirm efficiency of the proposed modification.     

Continuous approximation of linear impulsive systems and a new form of robust stability

The time-scale tolerance for linear ordinary impulsive differential equations is introduced. How large the time-scale tolerance is directly reflects the degree to which the qualitative dynamics of the linear impulsive system can be affected by replacing the impulse effect with a continuous (as opposed to discontinuous, impulsive) perturbation, producing what is known as an impulse extension equation. Theoretical properties related to the existence of the time-scale tolerance are given for periodic systems, as are algorithms to compute them. Some methods are presented for general, aperiodic systems. Additionally, sufficient conditions for the convergence of solutions of impulse extension equations to the solutions of their associated impulsive differential equation are proven. Counterexamples are provided.