Spring 2022 - STAT 831 G100

Statistical Theory II (4)

Class Number: 6914

Delivery Method: In Person

Overview

  • Course Times + Location:

    We, Fr 4:30 PM – 6:20 PM
    REMOTE LEARNING, Burnaby

  • Prerequisites:

    STAT 830 or permission from the instructor.

Description

CALENDAR DESCRIPTION:

Advanced mathematical statistics for PhD students. Topics in probability theory including densities, expectation and random vectors and matrices are covered. The theory of point estimation including unbiased and Bayesian estimation, conditional distributions, variance bounds and information. The theoretical framework of hypothesis testing is covered. Additional topics that may be covered include modes of convergence, central limit theorems for averages and medians, large sample theory and empirical processes.

COURSE DETAILS:

This course will cover measure theoretic probability, random variables, expectation, product spaces, independence, derivatives, conditional probability, characteristic functions, and limit theorems. While rigorous proof is emphasized as the way to understand the material, the material is based on a course that has been taught to students in statistics, mathematics, engineering and science for many years. Course work will be based on homework assignments.

Mode of Teaching: Both synchronous and asynchronous

Grading

  • Problems Sets 100%

Materials

REQUIRED READING:

A FIRST LOOK AT RIGOROUS PROBABILITY THEORY

BY ROSENTHAL, JEFFREY S.

PUBLISHER: WORLD SCIENTIFIC PUBLISHING CO PTE LTD

EDITION: 2ND


ISBN: 9789812703712

Graduate Studies Notes:

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.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN SPRING 2022

Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place.  Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.