@article{716143474dd2411aac39f47853c66d7a,
title = "A goodness-of-fit test for a varying-coefficients model in longitudinal studies",
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.",
keywords = "Empirical process, Monte Carlo approximation, Varying-coefficient longitudinal model",
author = "Xu, {Wang Li} and Lixing ZHU",
note = "Funding Information: The first author{\textquoteright}s research was supported by the National Natural Science Foundation of China (No. 10701079) and by the Humanities and Social Sciences Project of Chinese Ministry of Education (No. 08JC910002). This second author{\textquoteright}s research was partly supported by a grant from The Research Grants Council of Hong Kong, Hong Kong, P.R. China. The authors thank Colin O. Wu for giving us the {\textquoteleft}MACS Public Use Data Set Release PO4 (1984-1991).{\textquoteright}The thanks also go to the editor, the associate editor and two referees for their constructive comments and suggestions which led to significant improvement of presentation for an early manuscript.",
year = "2009",
month = may,
doi = "10.1080/10485250902721806",
language = "English",
volume = "21",
pages = "427--440",
journal = "Journal of Nonparametric Statistics",
issn = "1048-5252",
publisher = "Taylor and Francis Ltd.",
number = "4",
}