STAT 596 INTER STAT METHODS (16:960:596:01)

Meeting time and location:

Class Room: Hill Center Room 124

Time: Thursday, 12:05pm – 2:55pm (a break of 10 minutes in between)

(Note, we will meet on Sept 10^th and we will not meet on Dec 10^th)

Instructor:

Name: Minge Xie

Phone: (732) 445-2693 (extension TBA)

Email: mxie_at_stat.rutgers.edu

Office: 565 Hill Center

Office Hours: By appointment

Syllabus and Emphasis:

The emphasis of this course will be on various regression models, including linear, glm, and nonparametric models. Statistical software package R (www.r-project.org) is required. Topics covered in this course include:

• Histogram and density estimation
• Linear regression models
• Generalized linear models
• Nonlinear models
• Nonparametric regression
• Random effects and fixed effects models
• EM algorithm/Missing data/Latent variable models (if time allows)

Course Syllabus:

• Fall 2009 Syllabus

Textbook and references:

[Main]:

Modern Applied Statistics with S. Fourth Edition. By W. N. Venables and B. D. Ripley. (Springer).

[Linear Regression]:

Applied Linear Regression Models. By J. Neter, M.H. Kutner, Michael H., W. Wasserman. (Irwin/McGraw-Hill)

Linear Statistical Models. By J. H. Stapleton (Wiley)

[GLM]

Generalized Linear Models, Second Edition. By P. McCullagh and J. A. Nelder. (Chapman & Hall)

[Kernel Smoothing]

Kernel Smoothing. By M.P. Wand and M.C. Jones. (Chapman & Hall)

[GEE & Random Effects/Longitudinal data]

Analysis of Longitudinal Data. By P. Diggle, P. Heagerty, K.-Y. Liang, and S. Zeger. (Oxford University Press)

Hand-out Examples of S-code (from my 586 class; R-code is similar):

• Examples on basics
• Examples on linear regressions
• Examples on GLM

Homework Assignments:

• HomeworkNo1 (due date:10/01/09)
• HomeworkNo2 (due date:10/028/09)
• HomeworkNo3 (due date: 12/3/09)

Final Assignment:

TBA

Software links:

• Link to the R software (free) http://www.r-project.org/
• Link to the S+ software (free student version) http://elms03.e-academy.com/splus/

New York Times articles

• on Statistics:

http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=2&em

• on R:

http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?_r=1&ref=business

 
http://bits.blogs.nytimes.com/2009/01/08/r-you-ready-for-r/