Modeling Count Data
Dr. Joseph HilbeAim of Course:
This course deals with regression models for count data; i.e. models with a response or dependent variable data in the form of a count or rate. A count is understood as the number of times an event occurs; a rate as how many events occur within a specific area or time interval. The course will cover Poisson regression, the foundation for modeling counts, as well as extensions and modifications to the basic model. Extensions are required when the assumptions underlying the Poisson model are violated. Negative binomial regression is the foremost method used to extend the Poisson model. Since Poisson assumptions are rarely met in practice, substantial attention will be devoted to the negative binomial model and its variants.Who Should Take This Course:
Analysts and researchers in a wide variety of fields who are concerned with modeling counts and rates.For those enrolled in a Program of Advanced Statistical Studies, this is a required or elective course in the following Programs:
- Biostatistics (controlled trials) - elective
- Statistics for Social Sciences - elective
- Biostatistics (epidemiology) - required
Course Program:
The course is structured as follows:Course Outline: The course is structured as follows
SESSION 1: Overview of Count Models and Methods of Estimation
- Varieties of count model
- History of count models
- Derivation of GLM-based algorithm
- Derivation of maximum likelihood count models
- The exponential family
- Residual analysis for counts
- The nature of risk and risk ratios
- Poisson regression
- Problem of overdispersion: apparent vs real
- Negative Binomial Regression
- Binomial vs Count models
- Marginal effects/Discrete change
- Synthetic data modeling
- NB-1, NB-C, NB-H, NB-P models
- generalized Poisson
- zero-truncated models
- zero-inflated models
- hurdle models
- selection models
- censored count models
- Finite Mixture models
- Quantile count models
- Bivariate count models
- Handling endogeny
- Fixed effects count models
- Random effects count models
- GEE count models
- Mixed-effects and multilevel count models
- Exact Poisson and negative binomial models
- Bayesian count models
- Project preparation
The Instructor:
Dr. Joseph Hilbe is Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology. Since 1992 Prof. Hilbe has served as an Adjunct Professor of Statistics at Arizona State University. Prof Hilbe is an elected Fellow of the American Statistical Association, for which he was on the initial Health Policy Statistics section executive committee, and is an elected member (Fellow) of the prestigious International Statistical Institute, for which he is chair of the Astrostatistics committee and Network, the first global association of astrostatisticians. Dr. Hilbe has authored over one hundred journal articles and is currently on the editorial boards of a number of statistics journals. He was the first editor of the Stata Technical Bulletin (now Stata Journal) and from 1997-2009 was Software Reviews Editor for The American Statistician. Dr. Hilbe is author of both Logistic Regression Models(2009, Chapman & Hall/CRC) and Negative Binomial Regression (2007, Cambridge University Press), and, with James Hardin, is author of Generalized Estimating Equations (2003, Chapman & Hall/CRC) and Generalized Linear Models and Extensions (2001, 2007, Stata Press). His latest book is R for Stata Users (2010, Springer, with R. Muenchen).Organization of the Course:
The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.Certificates and Grades:
You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.Credit:
This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.Dates:
Oct. 22 - Nov. 19, 2010Click here to be notified of future course offerings.
Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.
Level:
Intermediate/AdvancedPrerequisite:
The equivalent of Introduction to Statistics 1: Inference for a Single Variable, and Introduction to Statistics 2: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners).For additional information about course prerequisites, click here.
Though it is not required for practical applications of material in this course, some familiarity with calculus (see statistics.com's brief Calculus Review course) is helpful for a complete understanding of model development.
Some familiarity with modeling - such as that provided in Introduction to Regression or Logistic Regression will also be helpful. Categorical Data Analysis or its equivalent will also be helpful in acquainting participants with the type of data involved.
Course Text:
J. Hilbe's Negative Binomial Regression (Cambridge Univ. Press). You should order it from Stata Press here or 800-782-8272.Software:
In some lessons, you will benefit from being able to implement models in a software program that is able to do GEE. The methods covered in this course will be illustrated in Stata; reference will also be made to SAS and LogXact. Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.Registration:
Register Online - $499Register Online (academic) - $399 (you must be affiliated with a college, university or high school)
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.
Consider registering for this course together with two other Modeling courses as part of our special 3 course package registration for tuition savings.
Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.
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