Quantitative Risk Assessment of FMD Virus Transmission via Water

Foot-and-mouth disease (FMD) is a viral disease of domesticated and wild cloven-hoofed animals. FMD virus is known to spread by direct contact between infected and susceptible animals, by animal products such as meat and milk, by the airborne route, and mechanical transfer on people, wild animals, birds, and by vehicles. During the outbreak of 2001 in the Netherlands, milk from dairy cattle was illegally discharged into the sewerage as a consequence of transport prohibition. This may lead to contaminated discharges of biologically treated and raw sewage in surface water that is given to cattle to drink. The objective of the present study was to assess the probability of infecting dairy cows that were drinking FMD virus contaminated surface water due to illegal discharges of contaminated milk. So, the following data were collected from literature: FMD virus inactivation in aqueous environments, FMD virus concentrations in milk, dilution in sewage water, virus removal by sewage treatment, dilution in surface water, water consumption of cows, size of a herd in a meadow, and dose-response data for ingested FMD virus by cattle. In the case of 1.6 x 10(2) FMD virus per milliliter in milk and discharge of treated sewage in surface water, the probability of infecting a herd of cows was estimated to be 3.3 x 10(-7) to 8.5 x 10(-5), dependent on dilution in the receiving surface water. In the case of discharge of raw sewage, all probabilities of infection were 100 times higher. In the case of little dilution in small rivers, the high level of 8.5 x 10(-3) is reached. For 10(4) times higher FMD virus concentrations in milk, the probabilities of infecting a herd of cows are high in the case of discharge of treated sewage (3.3 x 10(-3) to 5.7 x 10(-1)) and very high in the case of discharge of raw sewage (0.28-1.0). It can be concluded that illegal and uncontrolled discharges of contaminated milk into the sewerage system may lead to high risks to other cattle farms at 6-50 km distance of the location of discharge within one day. This clearly underlines current measures that prohibit such discharges, and also asks for strict control. This risk assessment clearly demonstrated the potential significance of FMD virus transmission via water, and the results will be useful on an international scale, and could also serve as a basis for other FMD risk-assessment models.     

Bayesian sample-size determination for one and two Poisson rate parameters with applications to quality control

We formulate Bayesian approaches to the problems of determining the required sample size for Bayesian interval estimators of a predetermined length for a single Poisson rate, for the difference between two Poisson rates, and for the ratio of two Poisson rates. We demonstrate the efficacy of our Bayesian-based sample-size determination method with two real-data quality-control examples and compare the results to frequentist sample-size determination methods.     

Goodness-of-fit Procedures for Copula Models Based on the Probability Integral Transformation

Abstract.  Wang & Wells [ J. Amer. Statist. Assoc. 95 (2000) 62] describe a non-parametric approach for checking whether the dependence structure of a random sample of censored bivariate data is appropriately modelled by a given family of Archimedean copulas. Their procedure is based on a truncated version of the Kendall process introduced by Genest & Rivest [ J. Amer. Statist. Assoc. 88 (1993) 1034] and later studied by Barbe et al . [ J. Multivariate Anal. 58 (1996) 197]. Although Wang & Wells (2000) determine the asymptotic behaviour of their truncated process, their model selection method is based exclusively on the observed value of its L ²-norm. This paper shows how to compute asymptotic p -values for various goodness-of-fit test statistics based on a non-truncated version of Kendall's process. Conditions for weak convergence are met in the most common copula models, whether Archimedean or not. The empirical behaviour of the proposed goodness-of-fit tests is studied by simulation, and power comparisons are made with a test proposed by Shih [ Biometrika 85 (1998) 189] for the gamma frailty family.     

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.     

Direct Inference, Probability, and a Conceptual Gulf in Risk Communication

Differences in the conceptual frameworks of scientists and nonscientists may create barriers to risk communication. This article examines two such conceptual problems. First, the logic of "direct inference" from group statistics to probabilities about specific individuals suggests that individuals might be acting rationally in refusing to apply to themselves the conclusions of regulatory risk assessments. Second, while regulators and risk assessment scientists often use an "objectivist" or "relative frequency" interpretation of probability statements, members of the public are more likely to adopt a "subjectivist" or "degree of confidence" interpretation when estimating their personal risks, and either misunderstand or significantly discount the relevance of risk assessment conclusions. If these analyses of inference and probability are correct, there may be a conceptual gulf at the center of risk communication that cannot be bridged by additional data about the magnitude of group risk. Suggestions are made for empirical studies that might help regulators deal with this conceptual gulf.     

双通道图像／数据传输系统

叙述了基于电视逆程数据插入技术的双通道图像／数据传输系统的设计及关键技术。该系统以电视信号为媒介，无需增加新的信道设备即可将模拟电视通道拓展为模拟／数字兼容通道，可提供传输速率达８６．４ｋｂ／ｓ的高速数据传输。系统性能可靠、传输速率快、纠错能力强，具有广阔的应用前景。     

Confidence Intervals Based on Local Linear Smoother

Point-wise confidence intervals for a non-parametric regression function in conjunction with the popular local linear smoother are considered. The confidence intervals are based on the asymptotic normal distribution of the local linear smoother. Their coverage accuracy is evaluated by developing Edgeworth expansion for the coverage probability. It is found that the coverage error near the boundary of the support of the regression function is of a larger order than that in the interior, which implies that the local linear smoother is not adaptive to the boundary in terms of coverage. This is quite unexpected as the local linear smoother is adaptive to the boundary in terms of the mean squared error.     

一种多波形雷达信号全数字产生及处理系统

提出并实现了兼容多种雷达脉压信号的全数字产生及处理系统。其信号包括线性调频信号、非线性调频信号及其多相码、泰勒（Ｔａｙｌｏｒ）四相码信号等。利用级联型ＦＩＲ滤波器ＩＭＳＡ１００组成数字复相关脉压网络对多种带宽（带发达５ＭＨｚ）脉压信号进行实时数字处理；分析了数字方法运用于波形合成中由于ＤＡＣ零阶保持特性造成的信号频谱高频失真的机理；采用提高ＤＡＣ转换速率以获得近似理想冲激串取样的方法，产生了多种性能稳定、波形质量高的宽带雷达脉压信号。文中给出了部分模拟与实验结果。     

Bayesian incorporation of repeated measurements in logistic discrimination

A common problem in medical statistics is the discrimination between two groups on the basis of diagnostic information. Information on patient characteristics is used to classify individuals into one of two groups: diseased or disease-free. This classification is often with respect to a particular disease. This discrimination has two probabilistic components: (1) the discrimination is not without error, and (2) in many cases the a priori chance of disease can be estimated. Logistic models (Cox 1970; Anderson 1972) provide methods for incorporating both of these components. The a posteriori probability of disease may be estimated for a patient on the basis of both current measurement of patient characteristics and prior information. The parameters of the logistic model may be estimated on the basis of a calibration trial. In practice, not one but several sets of measurements of one characteristic of the patient may be made on a questionable case. These measurements typically are correlated; they are far from independent. How should these correlated measurements be used? This paper presents a method for incorporating several sets of measurements in the classification of a case.     

Mixtures of Toxic Agents and Attributable Risk Calculations

Calculations of attributable risks have attracted increasing interest recently. However, these efforts have been limited to mostly one agent, radiation, and no interactions with effects of other toxic agents have been taken into account. This paper outlines a generic approach to the calculation of attributable risks for an exposure to several toxic agents and interaction effects associated with them. In this calculation, the partition of interaction terms between the agents responsible is of particular importance. At present, there are no rules on how to assign equitable shares, so one methodology will be proposed and others discussed briefly. For one example of an assignment, the standard errors of the attributable risks are determined in terms of the uncertainties of the input parameters, thus setting the stage for a comparison of the different shares of responsibility.