APM 696 (730). Advanced Regression Modeling Methods
Instructor
: Lianjun Zhang
Office
: Room 323 Bray Hall
Phone
: (315) 470-6558
E-Mail
: lizhang@esf.edu
Lecture
: Tue. Thur. 2:00-3:20 pm, Bray Room 315
Office Hours : Tue. Thur. 12:00 - 1:00 pm or by appointment
Pre-requisite : APM 630 or equivalent
Lecture Notes: available for purchase at the ESF Copy Center, Room 04, Bray Hall.
SAS 9.3 Online Docs at http://support.sas.com/documentation/93/index.html
Course Objectives:
APM 730 is designed for advanced graduate students in different study fields who need to
conduct more complicated regression analysis and modeling, such as generalized linear and
nonlinear models, variogram and kriging, linear mixed models for spatial data, spatial regression
models (spatial lag and spatial error models), local spatial statistics and models, and LOESS and
GAM. The course focuses on statistical concepts and theory, modeling strategies, and statistical
computation using SAS. Example programs will be given for each procedure discussed in the
course and the resultant computer output will be interpreted in detail.
Course Outline:
1. Introduction and Review of Basic Statistics
2. Review of Regression Modeling
3. General and Generalized Linear Models
4. Logistic Regression
5. Poisson Regression
6. Beta Regression
7. Quantile Regression
8. Linear Mixed Models (Longitudinal data)
9. Nonlinear Mixed Models
10. Variogram and Kriging
11. Linear Mixed Models (Spatial data)
12. Spatial Regression Models
13. Local Spatial Statistics and Models
14. Spline, LOESS and GAM
1/17
1/24
1/31
2/7
2/14
2/21
2/28
3/7
3/21
3/28
4/4
4/11
4/18
4/25
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Reference:
Fischer, M.M., and A. Getis (ed). 2010. Handbook of applied spatial analysis. Springer. 811p.
Fortheringham, A.S. and P.A. Rogerson (ed). 2009. The SAGE handbook of spatial analysis.
SAGE. 511p.
Fortheringham, A.S., C. Brunsdon, and M. Charlton. 2002. Geographically weighted regression:
the analysis of spatially varying relationships. John Wiley & Sons. 269p.
Greene, W.H. 1997. Econometric analysis. 3rd Ed. Prentice Hall. 1075p.
LeSage, J. and R.K. Pace. 2009. Introduction to spatial econometrics. Chapman & Hall / CRC.
354p.
Littell, R.C., G.A. Milliken, W.W. Stroup, R.D. Wolfinger, and O. Schabenberger. 2006. SAS
for mixed models. 2nd Ed. SAS Press. 814p.
McCulloch, C.E. and S.R. Searle. 2001. Generalized, linear and mixed models. John Wiley &
Sons. 325p.
Myers, R.H., D.C. Montgomery, and G.G. Vining. 2002. Generalized linear models. John Wiley
& Sons. 342p.
Schabenrberger, O. and C.A. Gotway. 2005. Statistical methods for spatial data analysis.
Chapman & Hall / CRC. 488p.
Evaluation:
Your progress will be evaluated by the following weights:
Homework / Assignment
100%
Note:
(1) No quiz, test or exam!
(2) You will have a homework or reading assignments for each chapter. Homeworks will
usually require statistical analysis and interpretation of the results. You may work with other
students on statistical computing and discussion of potential solutions. You will be expected to
submit your own report for the analysis results. Copying the report from each other is NOT
acceptable.
Grading System:
Your final grade will be determined as follows:
95 - 99
90 - 94
85 - 89
80 - 84
75 - 79
< 75
=
=
=
=
=
=
A
AB+
B
BF
Good Luck!
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