Customer Perceptions of Agency Risk Communication

A government agency commissioned a baseline study of how its customers view the agency's risk information. The 70% response rate to a mail survey allows analysis by subgroups representing customers'primary interests. Although this agency traditionally has been responsible for ensuring plant and animal health at the farm gate (or where imported), responses emphasized emerging customer concerns about the environment and human health. Customers think many risk communication activities are important, but that the agency is not especially effective in conducting those activities. Customers are moderately satisfied with much of the risk information they receive, although many have little contact from or interaction with the agency. Customers identified other sources they use, which suggest potentially effective channels for this agency's risk messages. The study provides a baseline for measuring change in the agency's risk communication effectiveness. It also can be a model when other organizations plan their own risk communication evaluations.     

Nonparametric estimation of the limiting availability

The point availability of a repairable system is the probability that the system is operating at a specified time. As time increases, the point availability converges to a positive constant called the limiting availability. Baxter and Li (1994a) developed a technique for constructing nonparametric confidence intervals for the point availability. However, nonparametric estimators of the limiting availability have not previously been studied in the literature. In this paper, we consider two separate cases: (1) the data are complete and (2) the data are subject to right censorship. For each case, a nonparametric confidence interval for the limiting availability is derived. Applications and simulation studies are presented.deceased after the paper was accepted     

THE REDUCED-RANK GROWTH CURVE MODEL FOR DISCRIMINANT ANALYSIS OF LONGITUDINAL DATA

This paper presents a method of discriminant analysis especially suited to longitudinal data. The approach is in the spirit of canonical variate analysis (CVA) and is similarly intended to reduce the dimensionality of multivariate data while retaining information about group differences. A drawback of CVA is that it does not take advantage of special structures that may be anticipated in certain types of data. For longitudinal data, it is often appropriate to specify a growth curve structure (as given, for example, in the model of Potthoff & Roy, 1964). The present paper focuses on this growth curve structure, utilizing it in a model-based approach to discriminant analysis. For this purpose the paper presents an extension of the reduced-rank regression model, referred to as the reduced-rank growth curve (RRGC) model. It estimates discriminant functions via maximum likelihood and gives a procedure for determining dimensionality. This methodology is exploratory only, and is illustrated by a well-known dataset from Grizzle & Allen (1969).     

Extreme-value analysis of canadian wind speeds

Estimates of extreme winds are essential for engineering design, but in preparing such estimates major statistical issues are encountered. In this case study, the analysts were provided with hourly readings on wind speed, wind direction, and barometric pressure at five Canadian stations for observation periods ranging over several recent decades. Their assignment was to calculate point and interval estimates of 10-, 20-, 50-, and 100-year return values (i.e., upper fractiles) for the wind speeds at these stations.     

Survival Analysis For A Sequenced Clinical Trial With Covariates

In this paper we discuss the survival analysis for a clinical trial in which treatment categories and general prognostic data are realised at different stages during a patient's survival time. In the light of possible strategies for the parsimonious modelling of such data, a corresponding sequence of illustrative analyses is presented. Detailed results are given for a weighted least squares analysis and these generally agree with those obtained by maximum likelihood.     

Robust scale estimation in the error-components model using the empirical characteristic function

Robust estimators of the scale parameters in the error-components model are described. The new estimators are based on the empirical characteristic functions of appropriate sets of residuals and are affine equivariant, consistent and asymptotically normal. The robustness of the new estimators is investigated via influence-function calculations. The results of Monte Carlo experiments and an example based on real data illustrate the usefulness of the estimators.     

Binary Event String Analysis: A Compact Numerical Representation of the Event Tree

While the event tree is a useful diagrammatic aid to an appreciation of various event sequence possibilities, it is of a nature that suggests no obvious manner in which the associated probability data may be compiled as computer input. Here, we propose a complementary numerical representation of scenario possibilities which incorporates probability data in a succinct fashion. While its mnemonic properties facilitate the logical development of a system's characteristics, its compactness and unambiguity permit its utilization directly as computer input.     

A comparison of regression models for ordinal data in an analysis of transplanted-kidney function

Three regression models for ordinal data, those of Fienberg, McCullagh, and Anderson, are applied to an analysis of kidney function among transplant recipients. The conclusions arising from each model are presented and contrasted.     

ROBUST COMPARISON OF DISPERSIONS FOR SAMPLES OF DIRECTIONAL DATA

Robust methods are proposed for testing whether several directional distributions on the unit p-sphere have comparable dispersions. The families of distributions considered are the Langevin for random vectors, and the Generalised Scheidegger-Watson for random axes, with specific interest in the Fisher and Watson distributions on the sphere. The methods are analogues of Levene's procedure for comparing variances of normal distributions.     

视频点击流数据挖掘在远程教育中的应用

Web数据挖掘已经成为数据挖掘技术一个重要的应用领域,文章主要通过对远程教育网站的视频点击流日志文件进行数据挖掘,寻求用户访问规律,构建动态的个性化教育网站.