Ph.D. candidate in Business Analytics


About Me

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.

Curriculum Vitae Github



Working Paper

Research In Progress

  • Why Does the Distress Anomaly Disappear?
    with Yuhang Xing, Yan Yu, On-Going.
  • Surrogate Residual of Multinomial Logistic Regression with Application to Discrete Choice Modeling,
    with Dungang Liu, On-Going.
  • Robust Estimation with Cell-Wise Contamination or Missing Values,
    with Yichen Qin, On-Going.
  • Additive Logistic Model with Macroeconomic Covariates for Corporate Bankruptcy Prediction.
    with Yan Yu, and Shaonan Tian, Writing.
  • What drivers Bankruptcy and Other Failures? An Analysis with Variable Selection.
    with Yan Yu, and Shaonan Tian, On-Going.


  • "Simultaneous Confidence Intervals Using Entire Solution Paths", Poster
    The Fourth Workshop on Higher-Order Asymptotics and Post-Selection Inference (WHOA-PSI), St. Louis, MO, August 2019.
  • "Simultaneous Confidence Intervals Using Entire Solution Paths", Slides
    ASA 2019 Joint Statistical Meeting, Denver, CO, July, 2019.
  • "What drivers Bankruptcy and Other Failures? An Analysis with Variable Selection",
    INFORMS Annual Meeting, Data Science Workshop (Peer-Reviewed), Phoenix, AZ, November 2018
  • "Additive Logistic Model with Macroeconomic Covariates for Corporate Bankruptcy Prediction",
    ASA 2017 Joint Statistical Meeting, Baltimore, MD, August, 2017.
  • "Amplifying Creativity in Big Data Era: Relationship between creativity and failure",
    The 7th International Forum on Statistics, Beijing, China, 2016


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.

Creativity Survey

It is an online interactive applications

Stock Display

It is an Shiny app that is used to display time-series data and forecasting of stocks.


It includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats.

Bankruptcy Prediction

It is live app showing probability of bankruptcy of public companies in US.


Latest and future possible teaching materials.

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Business Analytics I

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)

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Data Mining I & II
Teaching Assistant

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
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Forecasting and Risk Analysis

Summary: TBD... (Under construction)

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Secret to Success!

Stay Hungry, Stay Foolish, and Stay Creative!