Likelihood and higher-order approximations to tail areas: A review and annotated bibliography

Recent developments in higher-order asymptotic theory for statistical inference have emphasized the fundamental role of the likelihood function in providing accurate approximations to cumulative distribution functions. This paper summarizes the main results, with an emphasis on classes of problems for which relatively easily implemented solutions exist. A survey of the literature indicates the large number of problems solved and solvable by this method. Generalizations and extensions, with suggestions for further development, are considered.     

Bounding Analysis as an Inadequately Specified Methodology

The bounding analysis methodology described by Ha-Duong et al. (this issue) is logically incomplete and invites serious misuse and misinterpretation, as their own example and interpretation illustrate. A key issue is the extent to which these problems are inherent in their methodology, and resolvable by a logically complete assessment (such as Monte Carlo or Bayesian risk assessment), as opposed to being general problems in any risk-assessment methodology. I here attempt to apportion the problems between those inherent in the proposed bounding analysis and those that are more general, such as reliance on questionable expert elicitations. I conclude that the specific methodology of Ha-Duong et al. suffers from logical gaps in the definition and construction of inputs, and hence should not be used in the form proposed. Furthermore, the labor required to do a sound bounding analysis is great enough so that one may as well skip that analysis and carry out a more logically complete probabilistic analysis, one that will better inform the consumer of the appropriate level uncertainty. If analysts insist on carrying out a bounding analysis in place of more thorough assessments, extensive analyses of sensitivity to inputs and assumptions will be essential to display uncertainties, arguably more essential than it would be in full probabilistic analyses.     

Bayesian Measures of the Minimum Detectable Concentration of an Immunoassay

The minimum detectable concentration (MDC) is one of the most important properties of an assay. It is a statement about the smallest physical quantity an assay can reliably measure, and is used in assay design and quality control assessments. A plethora of measures of the MDC have been reported in a widely scattered literature. Many of these were developed at a time when accuracy and relevance had to be sacrificed for computational feasibility. This paper identifies limitations of existing measures and demonstrates how Bayesian inference may be used to overcome these limitations. Several new measures of the MDC are developed. These are conceptually simpler than existing measures, and are free of analytical approximations. The recent advances in Bayesian computation make them efficient to evaluate. A procedure developed in this paper measures the difference in the quality of two assays and shows that the new Bayesian measures perform better than existing measures.     

固体火箭发动机壳体的模态分析

不稳定燃烧是固体火箭发动机的一种不正常的工作状态,这种不稳定燃烧可使燃烧室压强振荡的振幅达到相当大的量级,使发动机产生强烈的振动,可能使燃烧室壳体或推进剂装药受到破坏,从而引起发动机爆炸.利用大型有限元分析软件AN SY S对固体火箭发动机壳体进行了模态分析,并获得了对其强度和性能影响强烈的频率,此结果对研究固体火箭发动机的动力学特性及不稳定燃烧进行了理论分析、预测和防止压强发生振荡具有重要作用.     

关联理论在大学英语听力教学中的运用

在听力理解中,听者不仅要获得录音材料的字面意义,还要理解话语的隐含意义。本文运用关联理论,探讨了在外语听力教学中,要使学生正确理解话语,应引导学生寻找话语的最佳关联,结合语境进行认知推理,从而提高学生的听力理解能力。     

ANOMALIES, PROBINGS, INSIGHTS: KEN FOREMAN'S ROLE IN THE SAMPLING INFERENCE CONTROVERSY OF THE LATE 20TH CENTURY

This paper is based on the author's recollections of work done for and with E.K. Foreman, and later developments of that work. It describes most particularly the author's early intimations of the potential for using prediction models in survey sampling, the reasons why the use of such models, especially for inferential purposes, was so strongly resisted in the second half of the 20th century, the manner in which randomization and prediction inferences can be combined, and some advantages flowing from that combination.     

逻辑方阵的普适性探讨

2 0世纪 90年代以来 ,逻辑学界对逻辑方阵的研究取得一定成果 ,打破了逻辑方阵仅适用于简单命题范围之内的狭隘认识。认为逻辑方阵适用于一切命题。但仍显不足。笔者以为 :逻辑方阵不仅适用于命题之间 ,还适用于推理与推理之间、命题与推理之间 ,具有真正意义上的普适性特征。     

解读英国刑法中的严格责任

关于严格责任,至今没有明确的定义。不同的学者基于不同的理念和认识标准对英美刑法中的严格责任提出了各种学说和观点,使严格责任的含义更加扑朔迷离。通过分析最具代表性和典型性的英国刑法中的严格责任,笔者试图明确严格责任的含义范围、适用规则以及其存在价值,并就此提出对我国刑法的立法设想。     

Estimation of Polygons and Areas

Abstract.  In forestry the problem of estimating areas is central. This paper addresses area estimation through fitting of a polygon to observed coordinate data. Coordinates of corners and points along the sides of a simple closed polygon are measured with independent random errors. This paper focuses on procedures to adjust the coordinates for estimation of the polygon and its area. Different new techniques that consider different amounts of prior information are described and compared. The different techniques use restricted least squares, maximum likelihood and the expectation maximization algorithm. In a simulation study it is shown that the root mean square errors of the estimates are decreased when coordinates are adjusted before estimation. Minor further improvement is achieved by using prior information about the order and the distribution of the points along the sides of the polygon. This paper has its origin in forestry but there are also other applications.     

Block empirical likelihood for longitudinal partially linear regression models

The authors propose a block empirical likelihood procedure to accommodate the within‐group correlation in longitudinal partially linear regression models. This leads them to prove a nonparametric version of the Wilks theorem. In comparison with normal approximations, their method does not require a consistent estimator for the asymptotic covariance matrix, which makes it easier to conduct inference on the parametric component of the model. An application to a longitudinal study on fluctuations of progesterone level in a menstrual cycle is used to illustrate the procedure developed here.