V-Splines and Bayes Estimate
Smoothing splines can be thought of as the posterior mean of a Gaussian process regression in a certain limit. By constructing a reproducing kernel Hilbert space with an appropriate inner product, the Bayesian form of the V-spline is derived when the penalty term is a fixed constant instead of a...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/78085 |
| Summary: | Smoothing splines can be thought of as the posterior mean of a Gaussian
process regression in a certain limit. By constructing a reproducing kernel
Hilbert space with an appropriate inner product, the Bayesian form of the
V-spline is derived when the penalty term is a fixed constant instead of a
function. An extension to the usual generalized cross-validation formula is
utilized to find the optimal V-spline parameters. |
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