Some Aspects of the Spline Smoothing Approach to Non-Parametric Regression Curve Fitting
Clicks: 3
ID: 298465
1985
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.6
/100
3 views
2 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #95 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 145 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
SUMMARY Non-parametric regression using cubic splines is an attractive, flexible and widely-applicable approach to curve estimation. Although the basic idea was formulated many years ago, the method is not as widely known or adopted as perhaps it should be. The topics and examples discussed in this paper are intended to promote the understanding and extend the practicability of the spline smoothing methodology. Particular subjects covered include the basic principles of the method; the relation with moving average and other smoothing methods; the automatic choice of the amount of smoothing; and the use of residuals for diagnostic checking and model adaptation. The question of providing inference regions for curves – and for relevant properties of curves – is approached via a finite-dimensional Bayesian formulation.
| Reference Key |
openalex_W1510444525
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | B. W. Silverman |
| Journal | Journal of the Royal Statistical Society Series B (Statistical Methodology) |
| Year | 1985 |
| DOI |
10.1111/j.2517-6161.1985.tb01327.x
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.