**Problem of the Week**

**Math Club**

**BUGCAT 2018**

**DST and GT Day**

**Number Theory Conf.**

**Zassenhaus Conference**

**Hilton Memorial Lecture**

You are here: Homepage » People » Ganggang Xu » Math 536 Non-parametric Smoothing and Semiparametric Regression.

people:gang:non-parametric_smoothing_and_semiparametric_regression

Math 536 Non-parametric Smoothing and Semiparametric Regression.

Spring 2017

**Instructor:**Ganggang Xu**Email:**gang@math.binghamton.edu**Phone number:**(607) 777-3550**Office:**WH-133**Meeting time & location:**MWF 2:20-3:20pm at WH 100E.**Office hours:**MW 3:30-5:00pm or by appointment.

If you need to reach me, please e-mail gang@math.binghamton.edu.

*Please include [Math536] in the subject line of your email, or your email may not be read promptly.*

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.**

- More advanced techniques of regression models: nonparameteric regression models, mixed effects models and Generalized linear models
- Proficient use of programming language R with applications to regression models.
- More advanced training in scientific writing.
- More advanced training in presentation.

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).

**Required text**- Wood (2006). Generalized Additive Models: an introduction with R

**Recommended additional reading**- James et al. (2013). An Introduction to Statistical Learning with Applications in R.
- Free PDF copy available online: http://www-bcf.usc.edu/~gareth/ISL/

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.

- Here is a pointer to R blogs.

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.**

**Homework (20%)**:- Assigned every week. Don't skimp on the homework if you want a good grade.
- You may discuss the problems with each other in general terms, but you must write your own solution.
- All sources, including friends and colleagues, must be cited.

**Midterm exam (25%)**: March 27th (tentative, subject to change)**Final Exam (25%)**: TBD**Team project (30%)**: Guidelines

Grading points:

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 :)

people/gang/non-parametric_smoothing_and_semiparametric_regression.txt · Last modified: 2017/01/17 20:20 by gang