# Zongming Ma • Associate Professor of Statistics

## Contact Information

468 Jon M. Huntsman Hall
3730 Walnut Street

Research Interests: high-dimensional statistics, machine learning, network data analysis, nonparametric statistics

## Education

PhD, Stanford University, 2010
BS, Peking University, 2005.

Wharton: 2010-present

## Current Courses

• ### STAT432 - Mathematical Statistics

An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures.

STAT432001 ( Syllabus )

• ### STAT515 - Adv Stat Inference I

STAT 515 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.

STAT515001 ( Syllabus )

## Past Courses

• ### STAT102 - INTRO BUSINESS STAT

Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

• ### STAT431 - STATISTICAL INFERENCE

Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 101 and 102.

• ### STAT432 - MATHEMATICAL STATISTICS

An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures.

• ### STAT515 - ADV STAT INFERENCE I

STAT 515 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.

• ### STAT925 - MULTIVARIATE ANALY: THEO

This is a course that prepares PhD students in statistics for research in multivariate statistics and high dimensional statistical inference. Topics from classical multivariate statistics include the multivariate normal distribution and the Wishart distribution; estimation and hypothesis testing of mean vectors and covariance matrices; principal component analysis, canonical correlation analysis and discriminant analysis; etc. Topics from modern multivariate statistics include the Marcenko-Pastur law, the Tracy-Widom law, nonparametric estimation and hypothesis testing of high-dimensional covariance matrices, high-dimensional principal component analysis, etc.

• ### STAT971 - INTRO TO LINEAR STAT MOD

Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.

• ### STAT991 - SEM IN ADV APPL OF STAT

This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

## Awards and Honors

• Sloan Research Fellowship, 2016
• NSF CAREER Award, 2014

## Activity

All Research

### Awards and Honors

Sloan Research Fellowship 2016
All Awards