A class of weighted dependence measures for bivariate failure time data

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.     

Practical Maximum Pseudolikelihood for Spatial Point Patterns(with Discussion)

This paper describes a technique for computing approximate maximum pseudolikelihood estimates of the parameters of a spatial point process. The method is an extension of Berman & Turner's (1992) device for maximizing the likelihoods of inhomogeneous spatial Poisson processes. For a very wide class of spatial point process models the likelihood is intractable, while the pseudolikelihood is known explicitly, except for the computation of an integral over the sampling region. Approximation of this integral by a finite sum in a special way yields an approximate pseudolikelihood which is formally equivalent to the (weighted) likelihood of a loglinear model with Poisson responses. This can be maximized using standard statistical software for generalized linear or additive models, provided the conditional intensity of the process takes an 'exponential family' form. Using this approach a wide variety of spatial point process models of Gibbs type can be fitted rapidly, incorporating spatial trends, interaction between points, dependence on spatial covariates, and mark information.     

On the coase theorem and coalitional stability: the principle of equal relative concession

The Coase theorem is argued to be incompatible with bargaining set stability due to a tension between the grand coalition and sub-coalitions. We provide a counter-intuitive argument to demonstrate that the Coase theorem may be in complete consonance with bargaining set stability. We establish that an uncertainty concerning the formation of sub-coalitions will explain such compatibility: each agent fears that others may `gang up' against him and this fear forces the agents to negotiate. The grand coalition emerges from the negotiations if each agent uses the principle of equal relative sacrifice to determine the actual allocation. We also establish the rational basis for the choice of the principle of equal relative concession by the negotiating agents. Hence we argue that the Coase theorem will be valid even if there are stable sub-coalitions.     

Analysis of Competing Risks by Using Bayesian Smoothing

We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Bayesian prediction of waiting times in stochastic models

The authors show how saddlepoint techniques lead to highly accurate approximations for Bayesian predictive densities and cumulative distribution functions in stochastic model settings where the prior is tractable, but not necessarily the likelihood or the predictand distribution. They consider more specifically models involving predictions associated with waiting times for semi‐Markov processes whose distributions are indexed by an unknown parameter θ. Bayesian prediction for such processes when they are not stationary is also addressed and the inverse‐Gaussian based saddlepoint approximation of Wood, Booth & Butler (1993) is shown to accurately deal with the nonstationarity whereas the normal‐based Lugannani & Rice (1980) approximation cannot, Their methods are illustrated by predicting various waiting times associated with M/M/q and M/G/1 queues. They also discuss modifications to the matrix renewal theory needed for computing the moment generating functions that are used in the saddlepoint methods.     

从原型理论看法语女性语言的形成

在社会语言学上,女性语言被看作是众多语言变体的一种,人们常常会从各个角度来探讨它形成的原因,而对它具体是如何形成的却很少提及。于是,文章在原型理论的框架下对法语女性语言原型的产生和这一语言变体的形成进行较为系统的描述,并通过对具体实例的分析,近距离地观察原型与范畴内其他成员的相似性并突出其在女性语言形成过程中不可磨灭的功绩。     

论二元户籍制度在中国的生成

我国二元户籍制度的构建过程分为三个阶段,即制度初建阶段、法制建设阶段和制度完善阶段。大量研究成果表明,二元户籍制度在中国的生成是经济、社会、政治及意识形态等因素综合影响的结果,其中政府最高决策层在制度选择过程中起到了决定性作用,并深刻影响了近三十年来的户籍制度改革进程。     

基于工作流的企业业务过程集成建模方法

工作流管理是一种对业务过程进行支持、控制、监视和优化的先进工具. 将生产制造过程 BOM和Petri 网技术应用于企业业务过程,对于相关定义、建模分析以及从BOM到Petri 网的映 射实现算法进行了说明;对基于BOM- Petri 的建模方法进行了扩展优化;最后结合算法进行了 仿真研究.     

局部平稳过程、稳定过程及GARCH模型的比较研究

本文在Granger（2005）[1]研究成果的基础上对局部平稳过程的大样本性质进行了深入探讨,发现了一些颇具实际价值的理论结果,弥补了Granger（2005）仅利用预测效果标准得到金融数据生成过程的不足,进一步给出了适合于实证分析的判断金融数据生成过程的标准,并在此基础上详细讨论了局部平稳过程、稳定过程及GARCH模型在大样本情况下的区别。本文运用研究得到的结果,在非平稳框架下对中国股票市场上证180指数进行了分析,发现上证180指数收益率具有明显的非平稳特性,并在此基础上进一步讨论了中国股票市场的市场有效性问题。