| Registro completo |
| Provedor de dados: |
AgEcon
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| País: |
United States
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| Título: |
Multivariable modeling with cubic regression splines: A principled approach
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| Autores: |
Royston, Patrick
Sauerbrei, Willi
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| Data: |
2011-12-27
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| Ano: |
2007
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| Palavras-chave: |
Mvrs
Uvrs
Splinegen
Multivariable analysis
Continuous predictor
Regression spline
Model building
Goodness of fit
Choice of scale
Research Methods/ Statistical Methods
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| Resumo: |
Spline functions provide a useful and flexible basis for modeling relationships with continuous predictors. However, to limit instability and provide sensible regression models in the multivariable setting, a principled approach to model selection and function estimation is important. Here the multivariable fractional polynomials approach to model building is transferred to regression splines. The essential features are specifying a maximum acceptable complexity for each continuous function and applying a closed-test approach to each continuous predictor to simplify the model where possible. Important adjuncts are an initial choice of scale for continuous predictors (linear or logarithmic), which often helps one to generate realistic, parsimonious final models; a goodness-of-fit test for a parametric function of a predictor; and a preliminary predictor transformation to improve robustness.
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| Tipo: |
Article
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| Idioma: |
Inglês
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| Identificador: |
st0120
http://purl.umn.edu/119254
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| Relação: |
Stata Journal>Volume 7, Number 1, 1st Quarter 2007
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| Formato: |
26
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