# Fall 2021 - STAT 830 G100

## Overview

• #### Course Times + Location:

We, Fr 10:30 AM – 12:20 PM
WMC 3533, Burnaby

• #### Prerequisites:

STAT 450 or permission of the instructor.

## Description

#### CALENDAR DESCRIPTION:

The statistical theory that supports modern statistical methodologies. Distribution theory, methods for construction of tests, estimators, and confidence intervals with special attention to likelihood and Bayesian methods. Properties of the procedures including large sample theory will be considered. Consistency and asymptotic normality for maximum likelihood and related methods (e.g., estimating equations, quasi-likelihood), as well as hypothesis testing and p-values. Additional topics may include: nonparametric models, the bootstrap, causal inference, and simulation. Students with credit for STAT 801 may not take this course for further credit.

#### COURSE DETAILS:

Course Outline:

We will be discussing how to develop and evaluate statistical methods. We survey various general statistical techniques: prediction, forecasting, point and interval estimation,  and  hypothesis  testing;  we  discuss  how to  assess how well  a specific technique works in repeated sampling terms: forecast standard error, standard error of estimation, coverage probabilities, error rates; we consider optimality theory; throughout we examine trade-offs: bias versus variability, type I versus type II error rates, interval coverage versus precision or length, mechanistic versus  empirical  models,  and  others. The vision is that we use the techniques of probability to discuss inference in the face of uncertainty. I will start with inference and fill in background in probability as needed. Our focus is chapters 6 through 11 of the Larry Wasserman's text All of Statistics but I don't think you really need the text.

1. Probability: random variable, expectation, inequalities, and convergence
2. Inference: Parametric and nonparametric models, empirical distribution function, bootstrap, maximum likelihood and related methods, properties of MLEs and related methods, hypothesis testing and p-values, simulation, selected topics.

Course Details:

• Lecture: During the pandemic I pre-recorded lectures which will be available on Canvas and on SFU’s Mediasite.  I expect you to watch the lecture before attending the corresponding classes in WMC 3533.
• Flipped Classroom: The structure is supposed to be a version of a flipped classroom.  I hope we will have lots of conversation in the classroom portion but I warn that I am perfectly capable of just talking all the time even if I don’t want that to happen.
• Website: On Canvas I will also put the slides for the recorded lectures and scans of things I write during lecture when possible.  I do not expect to be able to record the classroom presentations. My own website has lots of extras from older versions of the course.
• Office Hours: I will schedule a couple of office hours including 1 by zoom.   The zoom hour will be open to all – so more like an unstructured tutorial – if you need privacy please e-mail me to make a zoom appointment or try dropping in to my office.
• My presence on campus: probably not every day but obviously I will be there Wednesdays and Fridays.

Mode of Teaching:
• Lectures: In person.  Additionally: asynchronous pre-recorded videos.
• Tutorial: None.
• Quizzes and midterm: None.
• Final exam: In person. Date: TBA.
• Exceptions: Students who are unable to attend in person will watch the on-line recordings and do the homework. If on-line exams are necessary they will be administered through zoom and a high quality camera and microphone, both turned on, will be required.

• Assignments 70%
• Final exam 30%

#### NOTES:

Above grading is subject to change.

#### REQUIREMENTS:

Students will need high-speed internet access, a working camera and microphone. They will need to download and install R and I will encourage the use of RStudio.  You should also install a working TeX version or be prepared to use something like Overleaf.  These are all available on most computing platforms and are free in general for students.

## Materials

#### MATERIALS + SUPPLIES:

Textbook:

All Of Statistics: A Concise Course in Statistical Inference by Larry Wasserman. Publisher: Springer.

eBook: ISBN 978-0-387-21736-9
Softcover: ISBN 978-1-4419-2322-6
Harcover: ISBN 978-0-387-40272-7

Available in electronic form through lib.sfu.ca.  I won't be requiring this text but the notes follow it and refer to it from time to time.

Here is a list of texts which are available in the library and which cover many of the ideas I want to touch on.

Polansky, Alan M. Introduction to Statistical Limit Theory. (2011).
Abramovich, Felix and Ritov, Ya'acov. Statistical Theory: A Concise Introduction. (2013).
Silvey, S. D. Statistical Inference. (1975).
DasGupta, Anirban. Asymptotic Theory of Statistics and Probability. (2008).

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.