Technique-driven statistical analysis can be like the
blind men sent by the king to examine an elephant. The man who feels the leg says an elephant is
like a pillar, the one who feels the tails says it is like a rope, the one who
feels the tusk says it is like a pipe.
It is left to the king to integrate the accounts, which he does
masterfully (this tale is told by the king).
Dr. Joe Hilbe and Dr. James Hardin help to ensure that your own
statistical focus does not narrow too quickly with the help of online course "Generalized
Linear Models (GLM)". For more details please visit at http://www.statistics.com/glm.
"Generalized Linear Models GLM" (Hilbe and
Hardin) extends ordinary least squares (OLS) regression to incorporate
responses other than normal. This course
will explain the theory of generalized linear models (GLM), outline the
algorithms used for GLM estimation, and explain how to determine which
algorithm to use for a given data analysis.
GLM allows the modeling of responses, or dependent variables, that take
the form of counts, proportions, dichotomies (1/0), positive continuous values,
as well as values that follow the normal Gaussian distribution. Continuous
response variables, the log normal, gamma, log-gamma (survival analysis), and
inverse Gaussian cases are covered. Binomial (logit, probit, and others) as
well as count models (poisson, negative binomial, geometric) are also touched.
Joe Hilbe and James Hardin are the co-authors of
"Generalized Linear Models and Extensions" (Stata Press) as well as
"Generalized Estimating Equations" (CRC Press). They have lectured widely in these areas, and
have been instrumental in developing computer routines for these methods -
routines that have been incorporated into popular statistical software
programs.
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, and Adjunct
Professor of Statistics at Arizona State University. Dr. Hilbe has authored
over one hundred journal articles and is author of the COUNT package in R,
located on the CRAN website. He was also the first editor of theStata
Technical Bulletin (now Stata Journal) and from 1997-2009
was Software Reviews Editor forThe American Statistician. Dr.
Hilbe is Editor-in-Chief of the Springer Series in Astrostatistics.
Dr. James Hardin is a Research Associate
Professor at the University of South Carolina. Co-author (with Joseph Hilbe) of Generalized
Estimating Equations, Dr. Hardin is on the editorial board of The
Stata Journal and is the developer of the Stata GEE command, and with
Dr Hilbe is developer of the GLM command.
Who Should Take This
Course?
Analysts in any field who need to move beyond standard
multiple linear regression models for modeling their data.
Course Program:
Course outline: The course is structured as
follows
SESSION
1: General Overview of GLM
- Derivation of GLM functions
- GLM algorithms: OIM, EIM
- Fit and residual statistics
SESSION 2: Continuous Response Models
- Gaussian
- Log-normal
- Gamma
- Log-gamma models for survival analysis
- Inverse Gaussian
SESSION
3: Discrete Response Models
- Binomial models: logit, probit, cloglog,
loglog, others
- Count models: Poisson, negative binomial,
geometric
SESSION
4: Problems with Overdispersion
- Overview of ordered and unordered logit
and probit regression
- Overview of panel models
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).
Email: info@c-elt.com
Call: 020 66009116
Websites:
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