STAT 372: Mathematical Statistics
STAT 372: Mathematical Statistics
Welcome to the course website for STAT 372!
Information and resources for the course can be found on this page. Click on the section headings to expand them. For assignment submission and grades please see Canvas.
Announcements:
Week 1:
Course Description: Laws of large numbers, weak convergence, some asymptotic results, delta method, maximum likelihood estimation, testing, UMP tests, LR tests, nonparametric methods (sign test, rank test), robustness, statistics and their sensitivity properties, prior and posterior distributions, Bayesian inference, conjugate priors, Bayes estimators.
Prerequisites: STAT 266 or STAT 276.
Grading:
Grade breakdown
5 assignments for 35% of the total grade. The lowest assignment grade is dropped.
2 quizzes, each worth 15%.
The final exam is worth 35%.
Assignments: All assignments are to be submitted on Canvas. You may scan handwritten solutions or write up solutions in LaTeX (preferred). If you choose to write up your solutions by hand please make sure that they are legible. For coding questions please submit relevant code chunks and output as part of your solution, while also including your raw code in a separate file. Assignments are meant to be completed individually without the assistance from your peers or generative AI models.
Late policy: 25% is subtracted from the grade of a given assignment for every day that this assignment is late. Assignments are due at 11:59 PM MST on the day indicated in the syllabus.
Resources:
Textbook:
The required textbook for the course is Introduction to Mathematical Statistics, Eight Edition, R. Hogg, J McKean, and A. Craig, Prentice Hall, 2019.
We will be following this book closely so make sure you have access to a copy.
Other resources will be posted on the course website throughout the semester.
Software: We will be periodically using R throughout this course. Coding portions of assignments should be done in R.
Other resources: Another standard textbook on mathematical statistics at an upper-undergraduate level is Statistical Inference by Casella and Berger. There are many other good, but more advanced, textbooks on mathematical statistics that I am happy to point you towards if you are interested.
Class Time, Office Hours, and Contact Information:
Class time: Monday, Wednesday and Friday, 1:00-1:50 PM, BS M-141.
Office hours: Monday and Wednesday, 2:00-2:50 PM, U Commons 4-233.
My email is: mccorma2[AT]ualberta[DOT]ca
Tentative Outline:
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