Statistical modeling and experimental design with contributions in environment, health care, and e-commerce

Abstract

Design of experiment and statistical modeling have played an increasingly important role in science and business and received enormous attention from industries and research institutes. Motivated from real-world examples, this dissertation develops new statistical methodologies in the field of experimental design and causality inferences. First two chapters of this dissertation focus on online experimental design. E-commerce companies like Linkedin and Amazon perform hundreds of experiments each day, with the goal of testing certain website functions and design in order to best serve customers and maximize profits. New experiment design and testing scheme based on multi-armed bandit and conditional main-effect have been developed to let companies run experiment more efficiently. In chapter three, we develop a new statistical model based on combining information from physical experiment and computer experiment. The new method has been applied to model the Solar Irradiance data in the U.S. that were provided by IBM. Chapter four extends the linear G-formula method in the field of causality inference to non-linear set-up to study the causality relationship between physical activity level and health outcomes