Basic
modeling methods assume independent observations, but the real world is rarely
so conveniently arranged. Data within a school may be correlated (when
compared to observations from other schools in the study), likewise for patient
treatment centers in multi-center clinical trials. Learn how to deal with
data like this in "Modeling Longitudinal and Panel Data" with Joseph
Hilbe and James Hardin, in their online course July 1-29. Participants
can ask questions and exchange comments directly with both instructors via a
private discussion board throughout the period.
"Modeling Longitudinal and Panel Data" covers the extension of Generalized Linear Models (GLM) to model varieties of longitudinal and clustered data, called panel data. Specifically, the course treats generalized estimating equations (GEE), a population averaging method that models panel data in which the response is a member of the exponential family of distributions; e.g., continuous, binary, grouped, and count. The instructors will discuss GEE theory, relevant correlation structures, and differences in both theory and application between population averaging GEE (PA-GEE) and random effects or subject specific panel models (SS-GEE). This course covers model construction, how to estimate the equations, different types of models, how to deal with missing data, testing of models, model assumptions, and more. Familiarity with GLM (Generalized Linear Models) is a prerequisite.
Dr. Hilbe and Hardin are the authors of "Generalized Estimating Equations" (Chapman & Hall/CRC). 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. He is an elected Fellow of both the American Statistical Association and Royal Statistical Society. Dr. Hardin is on the faculty at the University of S. Carolina, and also on the board of the "Stata Journal."
"Modeling Longitudinal and Panel Data" covers the extension of Generalized Linear Models (GLM) to model varieties of longitudinal and clustered data, called panel data. Specifically, the course treats generalized estimating equations (GEE), a population averaging method that models panel data in which the response is a member of the exponential family of distributions; e.g., continuous, binary, grouped, and count. The instructors will discuss GEE theory, relevant correlation structures, and differences in both theory and application between population averaging GEE (PA-GEE) and random effects or subject specific panel models (SS-GEE). This course covers model construction, how to estimate the equations, different types of models, how to deal with missing data, testing of models, model assumptions, and more. Familiarity with GLM (Generalized Linear Models) is a prerequisite.
Dr. Hilbe and Hardin are the authors of "Generalized Estimating Equations" (Chapman & Hall/CRC). 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. He is an elected Fellow of both the American Statistical Association and Royal Statistical Society. Dr. Hardin is on the faculty at the University of S. Carolina, and also on the board of the "Stata Journal."
Aim of Course –
This
course covers the extension of Generalized Linear Models (GLM) to model
varieties of longitudinal and clustered data, called panel data. Specifically,
the course treats generalized estimating equations (GEE), a population
averaging method that models panel data in which the response is a member of
the exponential family of distributions; e.g., continuous, binary, grouped, and
count. GEE is one of several methods used to model panel data --- the most
noted alternative being random effect models.
The
course will discuss GEE theory, relevant correlation structures, and
differences in both theory and application between population averaging GEE
(PA-GEE) and random effects or subject specific panel models (SS-GEE).
Who Should Take This
Course –
Social
scientists, and medical and psychological researchers who need to analyze and
model longitudinal or panel data.
Course Program:
Course
outline: The course is
structured as follows
SESSION 1
- Theory and history of GLM
- Development of methods to analyze panel data
- Software used for GEE and related models
SESSION 2
- Model Construction and Estimating Equations for Panel data in
general and PA-GEE specifically
- Parameterization of the working correlation matrix
- Scale variance estimation
- Alternating logistic regression models
SESSION 3
- SS-GEE models (random effect)
- GEE2 models
- Generalized and cumulative logistic regression
- Problems with missing data
SESSION 4
- Residual analysis
- Goodness-of-fit
- Comparative testing of models
- MCAR assumption for PA-GEE models
The
core strength of these online courses is the instructors. Statistics.com has
over 60 instructors, who are recruited based in their expertise various areas
in statistics. Most of these instructors are internationally-known experts in
their field. These instructors are the subject experts with industry /
consulting experience.
You
will be able to ask questions and exchange comments with the instructors via a
private discussion board throughout the course. The courses take
place online at statistics.com in a series of 4 weekly lessons and assignments,
and require about 15 hours/week. Participate at your own convenience;
there are no set times when you must be online. You have the flexibility to
work a bit every day, if that is your preference, or concentrate your work in
just a couple of days.
For
Indian participants statistics.com accepts registration for its courses at
special prices in Indian Rupees through its partner, the Center for eLearning
and Training (C-eLT), Pune (www.c-elt.com).
For
India Registration and pricing, please visit us at www.india.statistics.com.
If
you have any query please feel free to call me or write to me.
For
More details contact at
Email: info@c-elt.com
Call:
020 66009116
Websites:
