ON THE COVERAGE PROBABILITY OF CONFIDENCE INTERVALS IN REGRESSION AFTER VARIABLE SELECTION

This paper considers a linear regression model with regression parameter vector β. The parameter of interest is θ= a^Tβ where a is specified. When, as a first step, a data‐based variable selection (e.g. minimum Akaike information criterion) is used to select a model, it is common statistical practice to then carry out inference about θ, using the same data, based on the (false) assumption that the selected model had been provided a priori. The paper considers a confidence interval for θ with nominal coverage 1 ‐ α constructed on this (false) assumption, and calls this the naive 1 ‐ α confidence interval. The minimum coverage probability of this confidence interval can be calculated for simple variable selection procedures involving only a single variable. However, the kinds of variable selection procedures used in practice are typically much more complicated. For the real‐life data presented in this paper, there are 20 variables each of which is to be either included or not, leading to 2²⁰ different models. The coverage probability at any given value of the parameters provides an upper bound on the minimum coverage probability of the naive confidence interval. This paper derives a new Monte Carlo simulation estimator of the coverage probability, which uses conditioning for variance reduction. For these real‐life data, the gain in efficiency of this Monte Carlo simulation due to conditioning ranged from 2 to 6. The paper also presents a simple one‐dimensional search strategy for parameter values at which the coverage probability is relatively small. For these real‐life data, this search leads to parameter values for which the coverage probability of the naive 0.95 confidence interval is 0.79 for variable selection using the Akaike information criterion and 0.70 for variable selection using Bayes information criterion, showing that these confidence intervals are completely inadequate.     

钒－4－（2－噻唑偶氮）－间苯二酚的络合吸附波研究

运用单扫描示波极谱法，在ｐＨ５．５的乙酸－乙酸钠介质中找到了钒（Ｖ）－４－（２－噻唑偶氮－）间苯二酚（ＴＡＲ）络合物灵敏的吸附波，发现Ｖ（Ｖ）的浓度在２×ｌ０－３～４×ｌ０－６ｍｏｌ·Ｌ－１范围内与波峰高成线性关系；此吸附波为不可逆的络合吸附波；Ｖ（Ｖ）－ＴＡＲ络合比为１：１．运用该法测定矿石中的微量钒，得到了满意结果。     

Conditional inference for subject-specific and marginal agreement: Two families of agreement measures

A two-stage procedure is described for assessing subject-specific and marginal agreement for data from a test-retest reliability study of a binary classification procedure. Subject-specific agreement is parametrized through the log odds ratio, while marginal agreement is reflected by the log ratio of the off-diagonal Poisson means. A family of agreement measures in the interval [-1, 1] is presented for both types of agreement. The conditioning argument described facilitates exact inference. The proposed methodology is demonstrated by way of an example involving hypothetical data chosen for illustrative purposes, and data from a National Health Survey Study (Rogot and Goldberg 1966).     

Ta-Ta_2O_5-半导体系統的失效机理Ⅰ.氧离子迁移引起的失效

本文从Ta_2O_5的微观分析出发,提出了系统的物理模型。根据这个模型,论述了系统的失效机理。联系实际系统中的常见缺陷,分析讨论了它的早期失效机理。     

B2B下服务质量对关系质量的影响研究

在B2B背景下,以认证行业的顾客为调研对象,探究了服务质量对关系质量的影响.在已有的研究基础上,界定了服务质量的4维度结构和关系质量的3维度结构,构建了服务质量对关系质量的影响模型.对认证行业的721家顾客企业的实证研究表明,过程服务能力对关系质量有着正向的影响,结果质量对关系质量中的合作性、适应性有正向的影响,对关系氛围则没有显著影响,而过程交互质量与潜在服务能力对关系质量的影响不显著.     

《离骚》的速度感

屈原《离骚》中的“行进者”形象和其行进中的“速度感”既是屈原生命观的体现,也是其审美意识的体现。诗人用现实形态和神话形态表现出的“行进”过程,再现了他的心路历程。在行进速度上,有着“两快一慢”的特征。“快“与“慢”的区别,真实体现出主人公思想深处情感的变化,这种节奏上的跌宕变化,极大地增添了全诗的艺术效果和悲剧气氛。     

全球金融危机过后中美两国对中美关系的构想及认识研究

世界金融危机后,随着美国国际地位的下滑和中国国力快速提升,美国对中国的戒心也随之不断增加,在这样的背景下,中美两国间的认识将会发生怎样的变化?对这一议题进行分析对于中美关系的未来展望意义重大。根据中美之间的相互认识与构想来看,中美两国在经济方面维持密切的合作关系,但中国和美国依然就在核心利益方面进行着相互竞争。而且可以预测在未来的国际舞台上,中国将以更积极的姿态,更明确的立场处理中美关系。因此,中国对于核心利益的坚定态度、中美间的矛盾,以及基于相互尊重的对等的中美关系,所有这些都必将让中美间的竞争更加激烈。     

冲击型负荷下的生产存储模型研究

建立了考虑冲击需求、变化生产率的最优生产存储库存策略. 其特点是:把一个生产周 期分为多个生产阶段,各个生产阶段时间可以不同;在每个生产阶段中,允许生产率小于需求 率,允许提前生产;而且,最大生产率要小于最大需求率. 研究发现,为满足冲击需求,提前存储 较多而启动机器数量较少,或提前存储较少而启动机器数量较多,不一定能够取得最佳效益, 要根据存储费用和启动费用共同确定. 在有限生产率的条件下,得出一种寻求最优生产- 库存 策略,为此类库存系统的管理决策提供了理论依据.     

Two Samples Tests for Functional Data

Data in many experiments arises as curves and therefore it is natural to use a curve as a basic unit in the analysis, which is in terms of functional data analysis (FDA). Functional curves are encountered when units are observed over time. Although the whole function curve itself is not observed, a sufficiently large number of evaluations, as is common with modern recording equipment, is assumed to be available. In this article, we consider the statistical inference for the mean functions in the two samples problem drawn from functional data sets, in which we assume that functional curves are observed, that is, we consider the test if these two groups of curves have the same mean functional curve when the two groups of curves without noise are observed. The L ²-norm based and bootstrap-based test statistics are proposed. It is shown that the proposed methodology is flexible. Simulation study and real-data examples are used to illustrate our techniques.     

Maximum Value of Hotelling's T 2 Statistics Based on the Successive Differences Covariance Matrix Estimator

In statistical process control applications, the multivariate T ² control chart based on Hotelling's T ² statistic is useful for detecting the presence of special causes of variation. In particular, use of the T ² statistic based on the successive differences covariance matrix estimator has been shown to be very effective in detecting the presence of a sustained step or ramp shift in the mean vector. However, the exact distribution of this statistic is unknown. In this article, we derive the maximum value of the T ² statistic based on the successive differences covariance matrix estimator. This distributional property is crucial for calculating an approximate upper control limit of a T ² control chart based on successive differences, as described in Williams et al. (2006 Williams , J. D. , Woodall , W. H. , Birch , J. B. , Sullivan , J. H. ( 2006 ). On the distribution of T ² statistics based on successive differences . J. Qual. Technol. 38 : 217 – 229 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]).