By James W. Hardin, Joseph M. Hilbe
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Extra resources for Generalized estimating equations MVsa
In this this parparrange restrictions observed . J,i EE (0,1) depends on on the the expected expected value value of of the the outcome outcome.. Therefore, Therefore, where pi (0,1) depends we should parameterize the linear predictor to enforce a range (0,1). we should parameterize the linear predictor to enforce a range (0,1) . The binomial binomial admits admits several several interesting interesting and and useful useful parameterizations parameterizations.. 38) 1-lei ( we have aa logistic, logistic, or or logit, logit, regression regression model.
Row of matrix X. We introduce introduce the the linear linear predictor predictor into into the the model model in in such way that that the the We such aa way range restrictions of of the the distribution distribution are are observed. observed. 26) V (yi) = Pi > 0 which depends on on the the expected expected value value of of the the outcome outcome.. In In fact, fact, for for the the Poisson Poisson which depends distribution, the variance variance is is equal equal to to the the expected expected value value..
If If the the obserobsercalled panel vations within panels panels come come from from the the same same experimental experimental unit unit measured measured over over vations within time, the the data data are are typically typically called called longitudinal longitudinal data. data. time, Unless aa model model or or method method is specific to to aa certain type of of panel panel structure, structure, Unless is specific certain type all forms forms of we adopt the the term term panel panel data data to to imply imply all of this this type type of of data.
Generalized estimating equations MVsa by James W. Hardin, Joseph M. Hilbe