The determination of the functional form of the relationship between an outcome variable and one or more continuous covariates is an important aspect of the modelling of medical data. For correct interpretation of the data it is essential that the functional form be speci"ed at least approximately c
β¦ LIBER β¦
Multivariate cubic spline smoothing in multiple prediction
β Scribed by Harry Khamis; Michael Kepler
- Book ID
- 114175229
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 96 KB
- Volume
- 67
- Category
- Article
- ISSN
- 0169-2607
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Choice of scale for cubic smoothing spli
β
Patrick Royston
π
Article
π
2000
π
John Wiley and Sons
π
English
β 192 KB
Predicting Multivariate Responses in Mul
β
Leo Breiman; Jerome H. Friedman
π
Article
π
1997
π
Blackwell Publishing
π
English
β 501 KB
Smoothing splines for trend estimation a
β
Richard Morton; Emily L. Kang; Brent L. Henderson
π
Article
π
2009
π
John Wiley and Sons
π
English
β 302 KB
## Abstract We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict
Reduction of the number of knots in the
β
VΔnceslava PretlovΓ‘; V. ΔervenΓ½
π
Article
π
1981
π
Springer
π
English
β 423 KB
Prediction of outcome in multiple sclero
β
BjΓΆrn Runmarker; Christer Andersson; Anders OdΓ©n; Oluf Andersen
π
Article
π
1994
π
Springer
π
English
β 695 KB
Evaluation of Two New Smoothing Methods
β
Zhongmin Cui; Michael J. Kolen
π
Article
π
2009
π
John Wiley and Sons
π
English
β 531 KB