Perfect simulation for correlated Poisson random variables conditioned to be positive

In this paper we present a perfect simulation method for obtaining perfect samples from collections of correlated Poisson random variables conditioned to be positive. We show how to use this method to produce a perfect sample from a Boolean model conditioned to cover a set of points: in W.S. Kendall and E. Thönnes (Pattern Recognition 32(9): 1569–1586, 1999), this special case was treated in a more complicated way. The method is applied to several simple examples where exact calculations can be made, so as to check correctness of the program using ²-tests, and some small-scale experiments are carried out to explore the behaviour of the conditioned Boolean model.     

Assessing convergence diagnostic tests for Bayesian Cox regression

The Markov chain Monte Carlo (MCMC) method generates samples from the posterior distribution and uses these samples to approximate expectations of quantities of interest. For the process, researchers have to decide whether the Markov chain has reached the desired posterior distribution. Using convergence diagnostic tests are very important to decide whether the Markov chain has reached the target distribution. Our interest in this study was to compare the performances of convergence diagnostic tests for all parameters of Bayesian Cox regression model with different number of iterations by using a simulation and a real lung cancer dataset.     

Bayesian analysis of directed graphs data with applications to social networks

Summary.  A fully Bayesian analysis of directed graphs, with particular emphasis on applica- tions in social networks, is explored. The model is capable of incorporating the effects of covariates, within and between block ties and multiple responses. Inference is straightforward by using software that is based on Markov chain Monte Carlo methods. Examples are provided which highlight the variety of data sets that can be entertained and the ease with which they can be analysed.     

Bayesian analysis of discrete time warranty data

Summary.  The analysis of warranty claim data, and their use for prediction, has been a topic of active research in recent years. Field data comprising numbers of units returned under guarantee are examined, covering both situations in which the ages of the failed units are known and in which they are not. The latter case poses particular computational problems for likelihood-based methods because of the large number of feasible failure patterns that must be included as contributions to the likelihood function. For prediction of future warranty exposure, which is of central concern to the manufacturer, the Bayesian approach is adopted. For this, Markov chain Monte Carlo methodology is developed.     

一种基于MPLS业务量工程的选路机制

分析了诸如受限路由等传统QoS路由机制业务震荡现象出现的原因给出了呼叫级QoS路由和分组级QoS路由的概念在此基础上提出了多标签QoS路由机制(MLR)作为一种分组级QoS路由机制多标签QoS路由机制不仅能够从理论上避免业务震荡现象的出现同时由于采用了业务量工程的业务均衡的思想从而提高了全网的业务性能指标仿真的数据结果可以在同时运行实时业务和非实时业务下得到     

上海证券市场GARCH效应检验和模型选择

文章利用1999年10月8日至2003年11月7日上证综合指数每日的收盘价数据对其进行了GARCH效应的检验,结果表明上海证券市场股价的波动存在着显著的GARCH效应,并且存在非对称的情况。在具体的模型选择上以EGARCH(1,1)较好,并且在研究中发现GARCH-M模型不适合模拟我国上海证券市场股价的波动情况。     

深圳证券交易所不同指数收益率的波动比较

股票价格指数的波动的大小通常代表了它的风险程度,研究股指的波动对风险管理、投资组合以及价格预测有着十分重要的意义。文章采用时间序列分析方法,对深圳证券交易所的三种价格指数建立GARCH模型,并对这三种指数的波动率进行比较。     

Further results in the optimal policy for imperfect inspection in discrete time

The paper reports investigations into how the optimal inspection interval for a fallible system with imperfect inspection is best determined. This work can be viewed as an extension of Raz and Ladany's 1992 work on imperfect inspection in discrete time. Here the cost functions are rewritten to include a probability conditioning that was omitted. In addition, an upper bound on the search space for the optimal inspection interval is presented. This bound allows one to find the global optimum with very modest computational effort. Insights gained from this bound and from the computational tests are used to present some general properties of the system.     

利率调整对股票市场的传导效应分析

文章以基于GARCH模型的事件研究法,分析近年来中国人民银行连续调整存贷款基准利率对股票市场的传导效应,研究股票市场对利率调整政策的反应速度和反应强度,检验股票市场的有效性,进而探索运用调整存贷款基准利率等货币政策管理股票市场风险的效力。实证结果表明,股票市场多次对利率上调呈利好反应,其原因可能与政策公告被股票市场投资者预测到有关。但利率调整目标的累积效应日趋明显,对股票市场过快发展有一定的抑制作用。     

基于马尔可夫链的产品市场保有量预测模型

售后服务机构为了准确地准备库存,都需要预测产品的市场保有量。基于马尔可夫链建立的状态概率矩阵模型,与抽样调查、时间序列法、回归分析法等方法相比,更能准确预测市场产品保有量。与传统产品相比较,更新换代速度快的电子产品,要预测其市场保有量,更需要利用状态概率矩阵模型。