I'm Xiaorui (Jeremy) Zhu, a Ph.D. candidate at the University of Cincinnati, Lindner College of Business. My major is Business Analytics. Before coming to the University of Cincinnati, I received an MS in Finance from Penn State University and a master of applied statistics from Beijing University of Technology.
My research interests include high dimensional statistics, machine learning, finance, relevant applications, and creativity. In high-dimensional statistics, I study the variable selection methods and the post-selection inference. One of my works proposed the sparsified simultaneous confidence intervals for the high-dimensional linear regression model (SSCI). In machine learning, I study robust estimation methods when data have missing values or contaminations. In finance, I work on volatility models and bankruptcy problems. In a published work, I proposed an adaptive method to estimate the coefficients of the GARCH model with heavy-tailed innovation. For the bankruptcy problem, I am interested in the prediction of bankruptcy. One of my projects focuses on the relationship between bankruptcy risk and stock returns. How creativity can stimulate the development of machine learning algorithms and artificial intelligence always arouse my interest.
I believe that stay Hungry, stay Foolish, and stay Creative is the secret to success. I dream of being a statistician engineering a machine intelligence that is the "epitome" of the descendants of human intelligence.
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
This is a course in the analysis of time series data with emphasis on appropriate choice of models for estimation, testing, and forecasting. It covers Univariate Box-Jenkins for fitting and forecasting time series; ARIMA models, stationarity and nonstationarity; diagnosing time series models; forecasting: point and interval forecasts; seasonal time series models; modeling volatility with ARCH, GARCH; modeling time series with trends; and other methods. The R Shiny App development will be covered to help students obtain skills of making prototype of their models and ideas.Undergraduate Graduate