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A boosting approach to flexible semiparametric mixed models
1Department of Statistics, Ludwigs-Maximilians-Universität, München, 80799, Germany. tutz@stat.uni-muenchen.de
Statistics in Medicine
|November 30, 2006
Summary
Likelihood-based boosting offers a powerful method for fitting additive mixed models, especially in high-dimensional data. This approach enables variable selection and automatic smoothing parameter tuning for enhanced statistical modeling.
Area of Science:
- Statistics
- Machine Learning
- Statistical Modeling
Background:
- Traditional linear mixed models (LMMs) assume parametric covariate influence.
- Semi- and non-parametric regression have expanded mixed models to include additive predictors.
- Additive models are commonly represented as mixed models.
Purpose of the Study:
- To introduce likelihood-based boosting as an alternative approach for additive mixed models.
- To leverage boosting techniques for high-dimensional settings with numerous explanatory variables.
- To develop flexible semiparametric mixed models accommodating subject-specific variation in smooth effects.
Main Methods:
- Likelihood-based boosting, an extension of L2 boost, is proposed.
- Component-wise boosting is utilized for its suitability in high-dimensional data.
- Boosting incorporates subject-specific variation through 'random slopes' on smooth effects.
Main Results:
- Additive models can be fitted for many covariates with implicit variable selection.
- Automatic selection of smoothing parameters is achieved.
- Flexible semiparametric mixed models are developed for complex subject-specific variations.
Conclusions:
- Likelihood-based boosting provides an effective method for fitting additive mixed models.
- The approach handles high-dimensional data and performs automatic variable and parameter selection.
- It enables the creation of flexible semiparametric models beyond simple random intercepts.
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