Xiaorui (Jeremy) Zhu is a Ph.D. student at University of Cincinnati, Lindner College of Business. His major is Business Analytics. Before coming to the University of Cincinnati, he received an MS in Finance from the Penn State University and a master of applied statistics from Beijing University of Technology.
His research interests include high dimensional statistics, machine learning, finance, relevant applications, and creativity. In high-dimensional statistics, he studies variable selection methods and the post-selection inference. One of his work proposed a general approach for constructing the simultaneous confidence inference for the sparse linear model. In machine learning, he studies robust estimation methods when data have missing values or contaminations. In finance, he studies volatility models and bankruptcy problem. In a published work, he proposed an adaptive method to estimate coefficients of the GARCH model with heavy-tailed innovation. For bankruptcy problem, he studies the prediction of bankruptcy. One work is focusing on the relationship between bankruptcy risk and stock returns. He is also interested in how creativity can stimulate the developing of machine learning algorithms and artificial intelligence.
He believes that stay Hungry, stay Foolish, and stay Creative is the secret to success. And he dreams of being able to create meaningful things that will benefit the general public.
I'm interested in making research results more intuitive and understandable. Therefore, I use Shiny app for interactively telling interesting data story. Here are some latest shiny apps and R package I created. Everyone interested in my research or software is welcome to contact me.
Latest and future possible teaching materials.
This course develops fundamental knowledge and skills for applying statistics to business decision making. Topics include descriptive statistics, probability distributions, sampling, confidence intervals, hypothesis testing and the use of computer software for statistical applications. (2018 Spring & Fall)Syllabus
The statiscial methods in these two courses include Linear Regression, Generalized Linear Models (e.g. Logistic regression), Variable Selection, Cross Validation, k-nearest neighbours, Classification and Regression Trees (CART), Bagging, Boosting, Random Forests, Generalized Additive Models (GAM for Nonlinearity), Nonparametric Smoothing; Neural Network, Clustering(K-means clustering, Support Vector Machine), Principal Component Analysis, Association Rules, and Text Mining.Syllabus Lab Notes