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.     

General m-esttmators and applications to bounded influence estimation for non-linear regression

In this paper, we study the M-estimators in the case that λ_F:(β)=E_F:(φ(Z,β))=0 has more than one solution, We show that the numerical iterative procedures converge and that the resulting estimators are consistent and asymptotically normal. We apply them to the non-linear regression models, and then, we find an optimal M-estimate among those that have bounded gross error sensitivity.     

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.     

On Calculations Involving the Maximum Cell Frequency

We describe a method of computing the cumulative distribution function of the maximum and minimum cell frequencies in sampling distributions commonly encountered in the analysis of categorical data.The procedure is efficient for exact or approximate calculation in both homogeneous and non-homogeneous cases, is non-recursive, and does not require Dirichlet integrals.Some related statistical problems are also discussed.     

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。     

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.     

Siting Noxious Facilities: A Test of the Facility Siting Credo

Over the past decade it has become increasingly difficult to site noxious facilities, despite the fact that there is a growing need to do so. To address this problem, a set of guidelines for a fairer, wiser, and more workable siting process—the Facility Siting Credo—was developed during a National Facility Siting Workshop in 1990. This paper presents an empirical test of these guidelines. A questionnaire based on the Credo was completed by stakeholders in 29 waste facility siting cases, both successful and unsuccessful, across the United States and Canada. Using an independent determination of outcome (success), a preliminary ranking of the importance of various Credo principles was obtained. The data reveal that establishing trust between the developer and host community is an important factor in facilitating the siting process. The siting process is most likely to be successful when the community perceives the facility design to be appropriate and to satisfy its needs. Public participation also is seen to be an important process variable, particularly if it encourages a view that the facility best meets community needs. Moreover, a siting process where communities volunteer to host facilities is an approach that holds promise for meeting many of these key success criteria.     

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。     

抗干扰、自适应、自组织、自恢复通信网技术

扼要介绍了抗干扰、自适应、自组织、自恢复通信网的组网原理和方法，提出了自组网算法，利用该算法完成了通信网网络拓扑结构的重组设计。