Ph.D Thesis Defense: "Topics in Complex and Large-scale Data Analysis"

Abstract: The past few decades have witnessed skyrocketed development of modern technologies. As a result, data collected from modern technologies are evolving towards a direction with more complicated structure and larger scale, driving the traditional data analysis methods to develop and adapt. In this dissertation, we study three statistical issues rising in data with complicated structure and/or in large scale. In Chapter 2, we propose a Bayesian framework via exponential random graph models (ERGM) to estimate the model parameters and network structures for networks with measurement errors; In Chapter 3, we design a novel network sampling algorithm for large-scale networks with community structure, and use it to estimate the number of distinct communities as an application; In Chapter 4, we introduce a proper framework to conduct discrete large-scale hypothesis testing procedure based on local false discovery rate (FDR). The performances of our procedures are evaluated through various simulations and real applications, while necessary theoretical properties are carefully studied as well.

Host: Nan Lin