I am teaching Mathematical Statistics this coming fall semester at University of Missouri. Please sign up for courses on the University website. If you have any specific questions about course topics, feel free to email me and I will respond as soon as possible.

STAT 8710: Intermediate Mathematical Statistics I

Semester: Fall

Offered: 2022, 2o23

This is a first-year graduate-level course in probability theory. Topics include sample spaces, probability and conditional probability, independence, random variables, expectation, distribution theory, sampling distributions, laws of large numbers and asymptotic theory, order statistics. Chapters 1 through 5 of Casella and Berger forms the basis of the course.

STAT 4610/7610: Applied Spatial Statistics

Semester: Spring

Offered: 2023

This course is designed as an intermediate introduction to the theory and applications of spatial statistics, which is open to senior undergraduate students, master's and Ph.D. students. Topics include methods for exploratory data analysis, model building, inference, and prediction of spatial data. We introduce and study the three areas of spatial analysis, include point processes, areal data, and geostatistical models. At the end of the course, we discuss extensions to these models/methods, including Bayesian inference, spatio-temporal models, etc. Class materials are supplemented with data examples and hands on applications in labs using R.

Useful Lab Links:

Lab 1 -  A Quick Introduction to R

Lab 2 -  Point-referenced Data Visualization

Lab 3 -  Areal Data Visualization

Lab 4 -  Simple Linear Regression & Raster Data Visualization

Lab 5 - Variogram

STAT 135: Concepts of Statistics       University of California, Berkeley

Semester: Summer

Offered: 2021

This course is an upper division course offered through the Department of Statistics on the theory and application of Statistical Inference. The topics covered includes survey sampling, parametric inference, Cramer-Rao inequality, sufficiency and completeness, hypothesis testing, ANOVA, linear models and introduction to Bayesian statistics. Overall responsibility includes the development and implementation of the course syllabus, the day-to-day delivery of the course and the issuing of grades.