Semiparametric Regression Using Variational Approximation
Semiparametric regression offers a flexible framework for modeling non-linear relationships between a response and covariates. A prime example are generalized additive models where splines (say) are used to approximate non-linear functional components in conjunction with a quadratic penalty to control for overfitting. Estimation and inference are then generally performed based on the penalized likelihood, or under a mixed model framework. The penalized likelihood framework is fast but...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of the American Statistical Association|
|Access Rights:||Open Access|
|ms-vagamsv7-unblinded.pdf||299.28 kB||Adobe PDF|
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