Overview

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. It is available electronically through the library.

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: This will be a standard in person lecture-based offering. During the pandemic I pre-recorded lectures which will be available on Canvas and on SFU’s Mediasite.
• Website: There will be a variety of material available in Canvas 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 be on campus every day in the fall and will hold office hours before the lectures.
• Assignments: These play a modest role in evaluation and a big role in learning. They are harder than exams.
• My presence on campus: every day except some Fridays.

Mode of Teaching:

• Lectures: In person.
• Tutorial: None
• Midterm: in person on October 13.
• Final exam: In person. Date: TBA.

Grading

Materials

MATERIALS + SUPPLIES:

Access to high-speed internet, webcam.

REQUIRED READING:

RECOMMENDED READING:

REQUIRED READING NOTES: