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 20% of the total grade. The lowest assignment grade is dropped.

• 2 in class quizzes, each worth 22.5%.

• 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. You should merge all your files together into one readable document; separate files for each question will not be accepted. 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 large language models.

Late policy: 25% is subtracted from the grade of a given assignment for every day that this assignment is late. Being late by one minute is equivalent to being late by a day. Assignments are due at 11:59 PM MST on the day indicated in the syllabus. For example, if an assignment is due on Wednesday and you hand it on on Friday at 1:00 PM you will obtain (Your Grade - 50%) on the assignment.  

Resources:

Textbook:

The suggested textbook for the course is Introduction to Mathematical Statistics, Eighth Edition, R. Hogg, J McKean, and A. Craig, Prentice Hall, 2019.

Course notes providing a sketch of what we covered in class can be found on the course website. 

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 11:50-12:50 (before class) and Wednesday, 2:00-2:50 PM (after class), U Commons 4-233. 

My email is: mccorma2[AT]ualberta[DOT]ca

• Assignment 1 

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• Week 1: 

• Code for convergence in distribution and probability

• Code for MLEs and likelihood.

• Code for hypothesis testing 

• Code for Bayesian analysis