Spring 2021 - STAT 843 G100
Functional Data Analysis (4)
Class Number: 3358
Delivery Method: In Person
Overview
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Course Times + Location:
Jan 11 – Apr 16, 2021: Wed, Fri, 10:30 a.m.–12:20 p.m.
Burnaby
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Instructor:
Haolun Shi
haoluns@sfu.ca
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Prerequisites:
STAT 830 or permission of the instructor.
Description
CALENDAR DESCRIPTION:
An introduction to smoothing and modelling of functional data. Basis expansion methods, functional regression models and derivative estimation are covered.
COURSE DETAILS:
1. Basis expansions and data smoothing
2. Roughness penalty smoothing
3. Bias/variance tradeoff and the smoothing parameter
4. Functional principal components
5. Functional regression models
6. Derivative estimation
7. Sparse Functional Data Analysis
8. Multivariate Functional Data Analysis
Mode of teaching:
This course only has lecture, and is project-based, i.e., no tutorial, midterm or finals.
- Lecture: Mix
- Tutorial: No tutorial
- Quizzes and Midterm: NA; Date: TBA
- Final exam: NA; date: TBA
- Remote invigilation (Zoom, or other approved software) will be used. NA
Grading
- Assignments 30%
- Project 70%
Materials
REQUIRED READING:
Ramsay, J. O. and B. W. Silverman (2005). Functional Data Analysis (Second ed.). New York: Springer
Book is Free online in the SFU Library
RECOMMENDED READING:
Applied Functional Data Analysis by James O. Ramsay & B.W. Silverman. Publisher: Springer. c2002
Functional Data Analysis with R and MATLAB by James O Ramsay & Giles Hooker & Spencer Graves. Publisher: Springer. c2009
Nonparametric Functional Data Analysis Theory and Practice by Frédéric Ferraty & Philippe Vieu. Publisher: Springer. c2006
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 2021
Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges 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. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.
Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).