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

从计数数据构建有效且精确的置信限的新的且计算上可行的方法。

• 批准号: DP0342873
• 负责人: A/Prof Paul Kabaila
• 金额: $ 10.79万
• 依托单位: La Trobe University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2003
• 资助国家: 澳大利亚
• 起止时间: 2003-05-31 至 2008-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0342873
• 关键词: New; computationally; feasible; methods; constructing

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.

生物和健康科学数据通常以计数的形式出现。对这些数据的统计分析应该是（a）有效的，即实际上不应该丢弃有价值的数据，（B）准确的，即应该具有精确已知的统计特性，以及（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造方法，该方法首次满足所有这些要求。该项目的目的是为新方法建立进一步的理论支持，开发有效的计算算法，并编写易于使用的计算机程序以供实际使用。