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Generalized Additive Models and Inflated Type I Error Rates of Smoother Significance Tests
Generalized additive models (GAMs) have distinct advantages over generalized linear models as they allow investigators to make inferences about associations between outcomes and predictors without placing parametric restrictions on the associations. The variable of interest is often smoothed using a...
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| Main Authors: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2011
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2952638/ https://ncbi.nlm.nih.gov/pubmed/20948974 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.csda.2010.05.004 |
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