V-Splines and Bayes Estimate
dc.contributor.author | Cao, Zhanglong | |
dc.contributor.author | Bryant, David | |
dc.contributor.author | Parry, Matthew | |
dc.date.accessioned | 2020-02-26T05:18:02Z | |
dc.date.available | 2020-02-26T05:18:02Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Cao, Z. and Bryant, D. and Parry, M. 2018. V-Splines and Bayes Estimate. arXiv.org. 1803.07645: pp. 1-16. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/78085 | |
dc.description.abstract |
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. | |
dc.subject | math.ST | |
dc.subject | math.ST | |
dc.subject | stat.TH | |
dc.title | V-Splines and Bayes Estimate | |
dc.type | Journal Article | |
dcterms.source.volume | 1803.07645 | |
dcterms.source.startPage | 1 | |
dcterms.source.endPage | 16 | |
dcterms.source.title | arXiv.org | |
dc.date.updated | 2020-02-26T05:18:00Z | |
curtin.department | School of Molecular and Life Sciences (MLS) | |
curtin.accessStatus | Fulltext not available | |
curtin.faculty | Faculty of Science and Engineering | |
curtin.contributor.orcid | Cao, Zhanglong [0000-0001-6667-9392] |