Abstract
In this article, regression splines are used inside linear mixed models to explore nonlinear longitudinal data. The regression spline bases are generated using a single knot chosen using biological information-a knot position supported by an automated knot selection procedure. A variety of inferential procedures are compared. The variance in the data was closely modeled using a flexible model-based covariance structure. a robust method and the nonparametric bootstrap, while the variance was underestimated when independent random effects were assumed.
| Original language | English |
|---|---|
| Pages (from-to) | 394-410 |
| Number of pages | 17 |
| Journal | Journal of Agricultural, Biological and Environmental Statistics |
| Volume | 10 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Dec 2005 |
Keywords
- AR(1) errors
- covariance structure
- linear mixed model
- nonparametric bootstrap
- robust inference
- serial correlation
- HEIGHT-DIAMETER MODEL
- LONGITUDINAL DATA
- CURVES
- IDENTIFICATION
- PENALTIES
Fingerprint
Dive into the research topics of 'Regression Spline Mixed Models: a forestry example'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver