Computer and biological experiments: Modeling, estimation, and uncertainty quantification

Computer experiments;Big data;Gaussian processes;Varying coefficients;Frailty models;Computer simulator selections

Lin, Li-Hsiang ; Wu, C. F. Jeff Joseph, Roshan Industrial and Systems Engineering Hung, Ying Huo, Xiaoming Zhu, Cheng ; Wu, C. F. Jeff

University:Georgia Institute of Technology

Department:Industrial and Systems Engineering

关键词: Computer experiments;    Big data;    Gaussian processes;    Varying coefficients;    Frailty models;    Computer simulator selections;   

Others  :  https://smartech.gatech.edu/bitstream/1853/63576/1/LIN-DISSERTATION-2020.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Statistical experimental analysis is an indispensable tool in engineering, science, bio-medicine, and technology innovation. There are generally two types of experiments: computer and physical experiments. Computer experiments are simulations using complex mathematical models and numerical tools, while physical experiments are actual experiments performed in a laboratory or observed in the field. Analyzing these experiments helps us understand real-world phenomena and motivates interesting statistical questions and challenges. This thesis presents new methodologies for applications in computer experiments and biomedical studies. In Chapters 1 and 2,we show that the concept of using transformation for improving the additivity of a target function is beneficial in computer experiments and big data modeling. In Chapter 3, motivated by a biological experiment, we propose a new method for quantifying uncertainty in biology studies. Chapter 4 addresses the problem of identifying an optimal computer simulator for the observed physical experiments.