%0 Journal Article %1 HastTibs:86 %A Hastie, T. %A Tibshirani, R. %D 1986 %J Statistical Science %K nonparametrics statistics %P 297--318 %T Generalized Additive Models %V 1 %X This article describes flexible statistical methods that may be used to identify and characterize the effect of potential prognostic factors on an outcome variable. These methods are called "generalized additive models", and extend the traditional linear statistical model. They can be applied in any setting where a linear or generalized linear model is typically used. These settings include standard continuous response regression, categorical or ordered categorical response data, count data,...

@article{HastTibs:86, abstract = {This article describes flexible statistical methods that may be used to identify and characterize the effect of potential prognostic factors on an outcome variable. These methods are called {"}generalized additive models{"}, and extend the traditional linear statistical model. They can be applied in any setting where a linear or generalized linear model is typically used. These settings include standard continuous response regression, categorical or ordered categorical response data, count data,...}, added-at = {2009-10-28T04:42:52.000+0100}, author = {Hastie, T. and Tibshirani, R.}, biburl = {https://www.bibsonomy.org/bibtex/26daad812668f25d271785055f642ba13/jwbowers}, citeulike-article-id = {99889}, date-added = {2007-09-03 22:45:16 -0500}, date-modified = {2007-09-03 22:45:16 -0500}, interhash = {05ecbce3b43a27ecf9c0c1363ba3a84b}, intrahash = {6daad812668f25d271785055f642ba13}, journal = {Statistical Science}, keywords = {nonparametrics statistics}, opturl = {http://citeseer.ist.psu.edu/14538.html}, pages = {297--318}, timestamp = {2009-10-28T04:42:58.000+0100}, title = {Generalized Additive Models}, volume = 1, year = 1986 }