Inference for earthquake models: A self-correcting model

Questions of asymptotic inference are discussed for a point process model in which the conditional intensity function increases monotonically between events and drops by determined (nonrandom) amounts after each event. Parameter estimates are shown to be consistent and, except under the null hypothesis of a Poisson process, normally distributed. Under the null hypothesis, however, the Hessian matrix is not asymptotically constant, and the limiting distribution of the likelihood ratio statistics is not χ², but has a form related to that of the Cramer-von Mises ω² statistic for the test of goodness of fit.     

Characterization of a class of bivariate distribution functions

Let F_X,Y(x,y) be a bivariate distribution function and P_n(x), Q_m(y), n, m = 0, 1, 2,…, the orthonormal polynomials of the two marginal distributions F_X(x) and F_Y(y), respectively. Some necessary conditions are derived for the co-efficients c_n, n = 0, 1, 2,…, if the conditional expectation E[P_n(X) ∥ Y] = c_nQ_n(Y) holds for n = 0, 1, 2,…. Several examples are given to show the application of these necessary conditions.     

Limit laws for upper and lower extremes from stationary mixing sequences

The aim of this paper is to examine the weak limiting behavior of upper and lower extremes from stationary sequences satisfying dependence conditions similar to D and D′ introduced by Leadbetter (Z. Wahrsch. Verw. Gebiete28 (1974), 289–303). By establishing the convergence in distribution of an associated sequence of point processes, the joint limiting distribution of any collection of upper and lower extremes can be determined. Sufficient and, in some cases, necessary conditions for the asymptotic independence of the upper and lower extremes are also given.     

On tail behavior of nonlinear autoregressive functional conditional heteroscedastic model with heavy-tailed innovations

We study the tail probability of the stationary distribution of nonparametric non- linear autoregressive functional conditional heteroscedastic (NARFCH) model with heavy- tailed innovations.Our result shows that the tail of the stationary marginal distribution of an NARFCH series is heavily dependent on its conditional variance.When the innovations are heavy-tailed,the tail of the stationary marginal distribution of the series will become heavier or thinner than that of its innovations.We give some specific formulas to show how the increment or decrement of tail heaviness depends on the assumption on the con- ditional variance function.Some examples are given.     

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.     

On the Non-parametric Prediction of Conditionally Stationary Sequences

We prove the strong consistency of estimators of the conditional distribution function and conditional expectation of a future observation of a discrete time stochastic process given a fixed number of past observations. The results apply to conditionally stationary processes (a class of processes including Markov and stationary processes) satisfying a strong mixing condition, and they extend and bring together the work of several authors in the area of non-parametric estimation. One of our goals is to provide further justification for the growing practical application of non-parametric estimators in non-stationary time series and in other `non-i.i.d.' settings. Some arguments as to why such estimators should work very generally in practice, often in a nearly `optimal' way, are given. Two numerical illustrations are included, one with simulated data and the other with oceanographic data. An erratum to this article is available at .     

On the parametrization of autoregressive models by partial autocorrelations

One of the difficulties that arise in the statistical analysis of autoregressive schemes is the very complex nature of the domain of the regression parameters. In the present paper we study an alternative parametrization of autoregressive models of finite order, namely the parametrization by the partial autocorrelations. These are shown to vary freely from −1 to +1 and to be in a one-to-one, continuously differentiable correspondence with the regression parameters. Properties of the asymptotic normal distribution of the maximum likelihood estimates are discussed, and we present a new deduction of Quenouille's result on the asymptotic independence of some of the estimated partial autocorrelations.     

On first passage time structure of random walks

For continuous time birth-death processes on {0,1,2,…}, the first passage time T⁺_n from n to n + 1 is always a mixture of (n + 1) independent exponential random variables. Furthermore, the first passage time T_0,n+1 from 0 to (n + 1) is always a sum of (n + 1) independent exponential random variables. The discrete time analogue, however, does not necessarily hold in spite of structural similarities. In this paper, some necessary and sufficient conditions are established under which T⁺_n and T_0,n+1 for discrete time birth-death chains become a mixture and a sum, respectively, of (n + 1) independent geometric random variables on {1,2,…};. The results are further extended to conditional first passage times.     

左截断相依数据下条件分位数的双核局部线性估计

本文在左截断相依数据下,利用局部线性估计的方法,先提出了条件分布函数的双核估计;然后利用该估计导出了条件分位数的双核局部线性估计,并建立了这些估计的渐近正态性结果;最后,通过模拟显示该估计在偏移和边界点调节上要比一般的核估计更好.     

基于GA-ELM的水上交通事故严重程度影响因素识别研究

水上交通事故严重程度影响因素的识别对从根本上减少严重事故件数、降低事故危害和损失具有重要意义。在历史事故报告的基础上,构建并量化事故影响因素集,提出以极限学习机(ELM)为一般事故、严重事故的二分类器,以遗传算法(GA)为因素搜索算法的GA-ELM因素识别模型。对发生在我国水域的737件水上交通事故进行实证研究,并与以支持向量机(SVM)为分类器的GA-SVM模型进行对比分析。结果表明,GA-ELM模型识别出时段、人为致因、环境致因等9个事故严重程度影响因素,较GA-SVM模型结果更为精简,且分类精度较不做因素识别时分别提高8.2%、7.1%。此外,GA-ELM大大缩短运算时间。由此可见,GA-ELM可为水上交通事故严重程度影响因素识别提供一个较好的方法。