New and computationally feasible methods of constructing efficient and exact confidence limits from count data.

新的和计算上可行的方法构造有效的和精确的置信限从计数数据。生物和健康科学数据通常以计数的形式出现。这种数据的统计分析应该是(a)有效的，即它实际上不应该丢弃有价值的数据；(b)精确的，即它应该具有精确已知的统计特性；(c)计算上可行。Kabaila和Lloyd（1997-2001）提出并分析了一种全新的置信限构建方法，该方法首次具备了所有这些要求。该项目的目的是为新方法建立进一步的理论支持，开发高效的计算算法，并为其实际应用编写易于使用的计算机程序。

New and computationally feasible methods of constructing efficient and exact confidence limits from count data. Biological and health science data is commonly in the form of counts. The statistical analysis of such data should be (a) efficient i.e. it should not, in effect, throw away valuable data, (b) exact i.e. it should have precisely known statistical properties and (c) computationally feasible. Kabaila and Lloyd (1997-2001) have proposed and analysed a radically new method of confidence limit construction which, for the first time, possesses all of these requirements. The purpose of the project is to establish further theoretical support for the new method, to develop efficient computational algorithms and to write easy-to-use computer programs for its practical use.