Quiz 1 will be in class on September 29th (50 minutes). Please bring your student ID to the exam. The quiz will test all of the material involving probabilistic convergence as well as some basic material on maximum likelihood estimation (material up to the class on Wednesday, September 24th). It will consist of long answer questions. You will be allowed one double-sided sheet of notes in the exam. I will provide relevant information and facts about probability distributions, so you do not need to memorize or transcribe their pdfs or pmfs, but you should know how to manipulate them. 

Practice problems for the quiz can be found here. These questions are either a little harder or of a similar difficulty to those you will find on the quiz. The quiz will have 5 questions. Solutions to the practice problems will not be provided; please come talk with me in office hours if you want to work through any of the problems.

I recommend that you do the practice problems, review the homework questions, review the questions in the textbook, and review your notes. You are expected to be able to prove statements about convergence in distribution and probability. However, I will make sure that such proofs are not as demanding as some of the proofs we have done in class. 

• Assignment 1 

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• Week 1: Intro and a brief review of some prerequisites. The definition of convergence in probability and consistency of an estimator. The weak law of large numbers along with Markov's and Chebyshev's inequalities. 

• Week 2: Rules for convergence in probability of sums, products, and continuous maps. Examples of consistent estimators, such as the sample variance. The definition of convergence in distribution and what it entails relative to convergence in probability (convergence in probability implies convergence in distribution). Examples of convergence in distribution. 

• Week 3: 

• Week 4:

• Week 5: 

• Week 6: 

• Week 7: 

• Week 8: 

• Week 9: 

• Week 10: 

• Week 11: 

• Week 12: 

• Week 13: 

• Week 14: 

• Code for convergence in distribution and probability

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 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 required textbook for the course is Introduction to Mathematical Statistics, Eighth 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 Friday, 2:00-2:50 PM, U Commons 4-233 (or possibly in the classroom if it is available). 

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