Unlocking the Power of Semiparametric Models: A Practical Tutorial for Analyzing Complex Data with Minimum Assumptions

Music City Center 

Room: CC-110B 

This short course will give biostatisticians and data scientists an engaging overview of the semiparametric modeling via real-world applications with complex structures, such as high-throughput sequencing and network data. Both classical and cutting-edge semiparametric techniques will be explored, highlighting their roles in balancing robustness, flexibility, and efficiency with minimum assumptions.

The foundation of statistical inference relies on models with explicit or implicit assumptions about the underlying data-generating process. Often, these models are characterized by finite-dimensional parameters. They have only limited robustness in practice, which championed the advancement of semiparametric modeling that blends finite-dimensional parameters of interest with infinite-dimensional nuisance parameters. Such flexibility has led to emerging applications in many research disciplines, evidently focusing on causal inference, missing data, survival, and survey studies.

This short course will break into two halves. The first half introduces the fundamental concepts of semiparametric models and outlines their roles in robust inference without and with missing data. Some recent advances will be discussed in the second half, covering diverse applications that scale up to the high-dimensional microbiome data and HIV viral genetic linkage networks while also scaling down to inferences encountering outliers and small sample sizes.