Non-Gaussian Model-Based Clustering

当代科学研究面临着不断增长的海量数据。鉴于科学家依靠统计数据来解释结果，适当的统计方法的可用性是至关重要的。不幸的是，由于缺乏适当的统计技术，一些研究问题目前无法回答。拟议的研究将开发将数据分组的新方法；这种方法被称为聚类或分类。这些技术一旦开发出来，将通过R软件的软件包发布给各个学科的研究人员使用。这项工作的影响将在生物信息学等学科中感受到，在这些学科中，它将有助于识别在一般情况下或在某些实验的时间过程中以类似方式表现的基因。这也将对食品科学和社会学等其他领域产生实际影响。

Contemporary science research is confronted with ever-growing masses of data. Given that scientists rely on statistics to interpret results, the availability of suitable statistical methods is essential. Unfortunately, some research questions cannot currently be answered due to the lack of appropriate statistical techniques. The proposed research will develop novel approaches to the assembly of data into groups; such approaches are known as clustering or classification. These techniques, once developed, will be released for use by researchers in every discipline through packages for the R software. The impact of this work will be felt in disciplines such as bioinformatics, where it will help identify genes that behave in a similar fashion either in general or over the time course of some experiment. There will also be real impact in other areas such as food science and sociology.