Generalized partially linear models
AbstractPartially linear models are linear regression models where one component is allowed to vary nonparametrically. Generalized partially linear models generalize this case from linear regression to the quasi-likelihood setting of standard GLIMs, thus encompassing a larger class models including logistic, Poisson, and Gamma regression. Although estimation for these models is possible in official Stata via fractional polynomials, this approach is entirely nonparametric and uses a local-linear smooth to estimate the "nonlinear" component. The Stata command gplm for fitting generalized partially linear models is discussed and demonstrated.
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Bibliographic InfoPaper provided by Stata Users Group in its series German Stata Users' Group Meetings 2004 with number 6.
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-07-26 (All new papers)
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