Predictive analysis and quality assessment in statistical learning

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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.