Please use this identifier to cite or link to this item: https://doi.org/10.1007/BF02741310
Title: SiZer for smoothing splines
Authors: Marron, J.S.
Zhang, J.-T. 
Keywords: Nonparametric tests
SiZer
Smoothing splines
Issue Date: 2005
Citation: Marron, J.S., Zhang, J.-T. (2005). SiZer for smoothing splines. Computational Statistics 20 (3) : 481-502. ScholarBank@NUS Repository. https://doi.org/10.1007/BF02741310
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. © Physica-Verlag 2005.
Source Title: Computational Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/105370
ISSN: 09434062
DOI: 10.1007/BF02741310
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

18
checked on Jun 15, 2018

WEB OF SCIENCETM
Citations

17
checked on May 21, 2018

Page view(s)

36
checked on May 11, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.