1 item has been added to your cart.
Generalized Estimating Equations, Second Edition |
||||||||||||||||||||||||||||||||||||
 Click to enlarge See the back cover |
As an Amazon Associate, StataCorp earns a small referral credit from
qualifying purchases made from affiliate links on our site.
VitalSource eBook details
VitalSource eBooks are read using the Bookshelf How to access your eBook1) Visit Bookshelf online to sign in or create an account.
2) Once logged in, click redeem in the upper right corner. Enter your eBook code. Your eBook code will be in your order confirmation email under the eBook's title.
3) The eBook will be added to your library. You may then download Bookshelf on other devices and sync your library to view the eBook.
Bookshelf is available on the following:
Bookshelf allows you to have 2 computers and 2 mobile devices activated at any given time.
I was amazed at the VitalSource way of presenting the books....
Everything looks perfectly typeset, but yet you can "flip"
through the book in the same way you would "flip" through a very
long web page in your web browser. And best of all, whenever I
have my tablet with me, my books are just a swipe away.
— Michael Mitchell
Senior statistician at the USC Children's Data Network, author of four Stata Press books, and former UCLA statistical consultant who envisioned and designed the UCLA Statistical Consulting Resources website. Return policy for eBooksStata Press eBooks are nonreturnable and nonrefundable. ×eBook not available for this title |
Review of the first edition from the Stata Journal |
||||||||||||||||||||||||||||||||||
Comment from the Stata technical groupThe method of generalized linear models (GLM) is an integral part of the data analyst’s toolkit, as it encompasses many models under one roof: logistic and probit regressions, ordinary least squares, ordinal outcome regression, and regression models for the analysis of survival data, to name a few. Nominal GLM, however, is inadequate when the data are longitudinal or are otherwise grouped so that observations within the same group are expected to be correlated. The method of generalized estimating equations (GEE) is a generalization of GLM that takes into account this within-group correlation. This text is the sequel to the 2001 text, Generalized Linear Models and Extensions, by the same authors, and provides the first complete treatment of GEE methodology. As with the previous text on GLM, this text is filled with examples on using this methodology with Stata. In fact, the principal author, James Hardin, developed much of the Stata software for fitting GEE models while he was a senior statistician at StataCorp. This text is heavy in mathematical and computational detail, but the mathematics is balanced by an array of real-world datasets and analyses. Thus the text should appeal to a wide audience, from the mathematical statistician wishing to glean the current state of the GEE literature to the professional researcher needing to fit a GEE model to solve a particular problem. The second edition includes material about estimation of GEEs for survival analysis and robust variance estimates, as well as additional model-selection tools. Additional program code has also been included. |
||||||||||||||||||||||||||||||||||||
Table of contentsView table of contents >> |
||||||||||||||||||||||||||||||||||||
 |
 |
 |
 |
 |
 |
)