Peter Hall Lecture
Jianqing Fan, Princeton University
Title: Communication-Efficient Accurate Statistical Estimation
Pao-Lu Hsu Award Lecture
Hongyu Zhao, Yale School of Public Health
Title: Fisher’s 1918 Quantitative Genetics Model In the Genomics Era
In 1918, R. A. Fisher reported a comprehensive study of a statistical model relating an individual’s quantitative traits to his/her genetic factors in his seminal paper entitled “The Correlation between Relatives on the Supposition of Mendelian Inheritance”. This model laid the foundation for the field of quantitative genetics. More than a century later, this model still proves effective in understanding the genetic basis of human complex traits when tens of thousands of chromosomal regions have been implicated for hundreds of traits through Genome-Wide Association Studies (GWAS) in the past 15 years. In this presentation, I will discuss how Fisher’s model has been used to quantify the genetic contributions to complex traits using GWAS results, its robustness to model misspecifications, and its extensions to identify relevant tissues/cell types for a specific trait and genetic correlations between different traits. I will also discuss statistical inference using either individual genotype and phenotype data, a typical set up for traditional statistical analysis, or summary statistics, which are more easily accessible for GWAS data. This is joint work with Can Yang, Jiming Jiang, Qiongshi Lu, Debashis Paul, Wei Jiang, Cecilia Dao, Yiliang Zhang, and others.
Zhiliang Ying, Columbia University
Title: Statistical models and methods for educational and psychological measurement
Statistical models have played important and fundamental roles in educational and psychological measurement. The increased computing power and data collection capability provide new opportunities as well as challenges. The first part of this talk covers the classical item response theory models which have been widely used in standardized testing, as well as recent developments on related multidimensional latent factor/class models with focus on important issues such as local independence or lack of it, identifiability among others. The second part covers modeling and analysis of process data arise from modern computer-based tests with items for assessing complex problem solving skills in technology-rich environments. Examples from educational assessment and psychological evaluation will be used throughout the presentation.