Math 536 Non-parametric Smoothing and Semiparametric Regression.
Math 531 and Math 532, or equivalent. A course in linear algebra. Graduate students from outside of the mathematical department and senior undergraduate students may take this course with Instructor's approval.
This course is a 4-credit course, which means that students are expected to do at least 12.5 hours of course-related work or activity each week during the semester. This includes scheduled class lecture/discussion meeting times as well as time spent completing assigned readings, studying for tests and examinations, preparing written and computing assignments, and other course-related tasks.
The required texts is Wood (2006) (see below for details).
1. R is chosen to be the statistical software used in this course. There are many online resources where the students can learn the basics of R.
Please install R before the beginning of the semester. In addition to R, some may find RStudio to be handy. Downloads:
Note: This is not an R class. R will not even be taught in class. You are expect to learning R programming by yourself.
2. All homework must be completed using Latex, unless otherwise instructed.
Slides (40%): you need to prepare slides that are clear, concise with great visualization of your results; Do not include too much technical detail;
Team presentation performance (30%, graded individually): you need to tell a story about your project; try to be as organized as possible and get right into the points; Your presentation should not be longer than 25 minutes; otherwise, you will be penalized;
Question/Answer performance (30%): you need to reserve 5 minutes for questions. The way you handle all questions must clearly show that you have sufficient knowledge of what you are doing. Otherwise, you will be penalized. I also encourage questions from the crowd, and if you ask a good question I will take a note in my heart :)