A goodness-of-fit test for a varying-coefficients model in longitudinal studies
Wang-Li Xu and
Li-Xing Zhu
Journal of Nonparametric Statistics, 2009, vol. 21, issue 4, 427-440
Abstract:
In this paper, we construct an empirical process-based test to examine the adequacy of a varying-coefficient model. A Monte Carlo approach is applied to approximate the null distribution of the test. Beyond the desired features that are shared by the existing empirical process-based tests, the Monte Carlo approximation makes the test self-invariant such that studentisation for the test statistic is not needed. Thus, the variance of residuals, as a studentising constant that is model dependent and may deteriorate the power of test, is no need to estimate. Simulations and an example are provided to illustrate our methodology.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://hdl.handle.net/10.1080/10485250902721806 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:21:y:2009:i:4:p:427-440
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485250902721806
Access Statistics for this article
Journal of Nonparametric Statistics is currently edited by Jun Shao
More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().