Name
Abstract | ||
|---|---|---|
Smoothing splines are an attractive method for scatterplot smoothing. The SiZer approach to statistical inference is adapted
to this smoothing method, named SiZerSS. This allows quick and sure inference as to “which features in the smooth are really
there” as opposed to “which are due to sampling artifacts”, when using smoothing splines for data analysis. Applications of
SiZerSS to mode, linearity, quadraticity and monotonicity tests are illustrated using a real data example. Some small scale
simulations are presented to demonstrate that the SiZerSS and the SiZerLL (the original local linear version of SiZer) often
give similar performance in exploring data structure but they can not replace each other completely. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/BF02741310 | Computational Statistics |
Keywords | DocType | Volume |
sizer,nonparametric tests,smoothing splines | Journal | 20 |
Issue | ISSN | Citations |
3 | 1613-9658 | 1 |
PageRank | References | Authors |
0.47 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. S. Marron | 1 | 1 | 0.47 |
| Jin-Ting Zhang | 2 | 9 | 3.20 |