Predictive analysis and quality assessment in statistical learning

预测建模是数据挖掘和统计学习领域的一个中心话题，研究人员和从业者寻求先进的分析工具和计算方法，以更好地利用数据资源。在许多应用中，开发考虑不同类型和特征的数据的有效系统是至关重要的。我的拟议研究方案预计将通过开发和应用统计学习和评估过程研究中的分析方法和计算方法，在这一研究领域作出重大贡献。更具体地说，我和我的研究团队将集中在以下三个主题上：(1)评估/改进概率分类器的可靠性；(2)保险中的稀疏标签数据分析；以及(3)预先训练的语言模型在自动调查编码中的应用。主题(1)研究预测模型评估，特别关注概率多项式分类器。主题(1)的成果将有助于发展评估监督分类中预测不确定性的理论和方法。在主题(2)中，根据最近大型可变年金投资组合的估值趋势，将设计和审查新的高效数据挖掘框架，以减轻这一问题的沉重计算挑战。我提出的研究计划的另一个贡献，也是主题(3)的重点，是让调查研究人员更深入地了解最近预培训的语言模型和工具，以进行有效的调查编码。这项研究将为包括数据挖掘、保险/金融和商业分析在内的各个领域的研究人员和从业者提供创新的见解，并培养准备在相关研究领域和高科技行业领导的高素质人才。

Predictive modeling is a central topic in the fields of data mining and statistical learning, where researchers and practitioners seek the advanced analytic tools and computational methods for better use of data resources. In many applications, it is of utmost importance to develop effective systems taking different types and characteristics of data into account. My proposed research program is expected to make significant contributions in this research field through the development and application of analytical approaches and computational methodologies in the study of statistical learning and assessment processes. More specifically, my research team and I will concentrate on the following three themes: (1) Assessment/improvement of the reliability of probabilistic classifiers; (2) Sparse-labeled data analysis in insurance; and (3) Application of pre-trained language models to automatic survey coding. Theme (1) studies predictive model assessment with a special focus on probabilistic multinomial classifiers. The outcomes from Theme (1) will contribute to the development of the theory and methodology in assessing prediction uncertainty in supervised classification. In Theme (2), following the recent trend in valuation of large variable annuity portfolios, new efficient data mining frameworks will be designed and examined to mitigate the heavy computational challenge of the problem. Another contribution of my proposed research program, which is the focus of Theme (3), is to offer survey researchers a deeper understanding of recent pre-trained language models and tools for effective survey coding. This research will provide innovative insights for researchers and practitioners in various fields including data mining, insurance/finance and business analytics, and train highly qualified personnel who will be prepared to lead in the related research areas and the high-tech industry.