Mathematical Sciences: Generalized Linear Models

描述复杂数据体的能力， 数学模型是一项重要而富有挑战性的工作。 的 该奖项的研究者扩展了模型拟合的视野。 广义模型拟合的工作可望有很大的发展 影响数据分析。 调查将考虑对滋扰的估计 分散参数的部分贝叶斯方法。 它还将 检查调整后的轮廓可能性之间的关系 以及改进的轮廓似然方法。

The ability to describe complex bodies of data by mathematical models is an essential and challenging task. The investigator of this award expands the horizon for model fitting. The work on generalized model fitting is expected to have great impact on data analysis. The investigation will consider the estimation of nuisance dispersion parameters by partially Bayes methods. It will also examine the relationship between the adjusted profile likelihood and the modified profile likelihood methods.