Time Series Models for Count or Qualitative Observations

Time series sometimes consist of count data in which the number of events occurring in a given time interval is recorded. Such data are necessarily nonnegative integers, and an assumption of a Poisson or negative binomial distribution is often appropriate. This article sets ups a model in which the level of the process generating the observations changes over time. A recursion analogous to the Kalman filter is used to construct the likelihood function and to make predictions of future observations. Qualitative variables, based on a binomial or multinomial distribution, may be handled in a similar way. The model for count data may be extended to include explanatory variables. This enables nonstochastic slope and seasonal components to be included in the model, as well as permitting intervention analysis. The techniques are illustrated with a number of applications, and an attempt is made to develop a model-selection strategy along the lines of that used for Gaussian structural time series models. The applications include an analysis of the results of international football matches played between England and Scotland and an assessment of the effect of the British seat-belt law on the drivers of light-goods vehicles.     

Inference in Targeted Group‐Sequential Covariate‐Adjusted Randomized Clinical Trials

This article is devoted to the construction and asymptotic study of adaptive, group‐sequential, covariate‐adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group‐sequentially, a sampling design that targets a user‐supplied optimal covariate‐adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group‐sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis‐specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.     

Covariate Selection for the Semiparametric Additive Risk Model

Abstract.  This paper considers covariate selection for the additive hazards model. This model is particularly simple to study theoretically and its practical implementation has several major advantages to the similar methodology for the proportional hazards model. One complication compared with the proportional model is, however, that there is no simple likelihood to work with. We here study a least squares criterion with desirable properties and show how this criterion can be interpreted as a prediction error. Given this criterion, we define ridge and Lasso estimators as well as an adaptive Lasso and study their large sample properties for the situation where the number of covariates p is smaller than the number of observations. We also show that the adaptive Lasso has the oracle property. In many practical situations, it is more relevant to tackle the situation with large p compared with the number of observations. We do this by studying the properties of the so-called Dantzig selector in the setting of the additive risk model. Specifically, we establish a bound on how close the solution is to a true sparse signal in the case where the number of covariates is large. In a simulation study, we also compare the Dantzig and adaptive Lasso for a moderate to small number of covariates. The methods are applied to a breast cancer data set with gene expression recordings and to the primary biliary cirrhosis clinical data.     

On the improvement of one-sided S control charts

The most common charting procedure used for monitoring the variance of the distribution of a quality characteristic is the S control chart. As a Shewhart-type control chart, it is relatively insensitive in the quick detection of small and moderate shifts in process variance. The performance of the S chart can be improved by supplementing it with runs rules or by varying the sample size and the sampling interval. In this work, we introduce and study one-sided adaptive S control charts, supplemented or not with one powerful runs rule, for detecting increases or decreases in process variation. The properties of the proposed control schemes are obtained by using a Markov chain approach. Furthermore, a practical guidance for the choice of the most suitable control scheme is also provided.     

功能性能分离的可信服务构件模型

为了向分布式实时嵌入式系统(DRES)的运行过程提供可信保障服务,方便上层应用程序的开发,提出了一种针对DRES的可信构件服务模型QUOCCM。该模型将系统功能实现与性能确保分离开,把应用程序的性能确保模块抽象成通用的服务确保构件,为运行于动态环境下的应用程序提供自适应的可信保障服务。该文以一个模拟实现的飞行控制系统验证了QUOCCM模型的可行性和灵活性。     

多项目管理在大型建筑企业工程项目中的应用研究

本文针对我国当前大型建筑施工企业的工程项目管理,对多项目管理的控制管理应用研究,通过建立项目办公室,管理区域市场,有效地协调分配现有的资源,解决需求的冲突,开发出可靠和可重复使用的系统。     

智能体建模和资本市场复杂性

以复杂适应系统的思想和智能体建模的方法研究资本市场复杂性是新兴的有价值的研究领域,阐述了资本市场作为复杂适应系统的动力机制,介绍了这一领域一个重要模型———少数派博弈(minority game,MG)模型.仿真发现,处于拥挤阶段的MG具有和实际市场相近的收益率分布.进一步扩展标准MG,提出了快速适应的少数派博弈模型,仿真结果显示,新的模型有着和真实市场相同的特征:收益率分布的尖峰和肥尾现象,揭示了资本市场复杂性的内部动力学机理.     

在国家创新系统内优化配置科技资源

科技资源、科技财力资源和科技人力资源是当今的“第一资源”. 因而如何优化配置科技 资源已成为决策者、管理者提高国家竞争力、企业竞争力的核心问题. 本文提出在“国家创新系 统”中优化配置科技资源的观点, 并从实践方面, 包括我国科研院所转制、中国科学院的知识创 新工程和科技中介机构建设等加以论证.     

矿产资源代际配置的若干问题研究

本文阐述了矿产资源代际公平配置理念 ,探讨了公正储蓄率与真正储蓄内涵、提出了真正储蓄为非负的两个必要条件 ,分析研究了贴现率与矿产资源可持续利用的关系及调节资源利用贴现率的途径。     

两类广义预测控制算法的统一推广

基于ＣＡＲＩＭＡ模型和ＡＲＭＡＸ模型的广义预测控制算法是两类基本的ＧＰＣ算法，给出了它们的统一格式，对ＧＰＣ算法的理论分析和实际应用有意义。