RT Journal Article
T1 A new lack-of-fit test for quantile regression with censored data
A1 Conde Amboage, Mercedes
A1 Keilegom, Ingrid van
A1 González Manteiga, Wenceslao
K1 Bootstrap calibration
K1 Censored data
K1 Lack-offit tests
K1 Quantile regression
AB A new lack-of-fit test for quantile regression models will be presented for the case where the response variable is right-censored. The test is based on the cumulative sum of residuals, and it extends the ideas of He and Zhu (2003) to censored quantile regression. It will be shown that the empirical process associated with the test statistic converges to a Gaussian process under the null hypothesis and is consistent. To approximate the critical values of the test, a bootstrap mechanism will be used. A simulation study will be carried out to study the performance of the new test in comparison with other tests available in the literature. Finally, a real data application will be presented to show the good properties of the new lack-of-fit test in practice.
PB Scandinavian Journal of Statistics
SN 0303-6898
YR 2021
FD 2021-01-10
LK http://hdl.handle.net/10347/32561
UL http://hdl.handle.net/10347/32561
LA eng
NO Conde-Amboage M, Van Keilegom I, González-Manteiga W.A new lack-of-fit test for quantile regression with censored data.Scand J Statist.2021;48:655–688. https://doi.org/10.1111/sjos.12512
NO The authors gratefully acknowledge the support of Project MTM2016-76969-P (Spanish State Research Agency, AEI) cofunded by the European Regional Development Fund (ERDF). The work of Mercedes Conde-Amboage has been supported by a postdoctoral grant from the Ministry of Culture, Education and University Planning and the Ministry of Economy, Employment and Industry of the Galician Government. Ingrid Van Keilegom gratefully acknowledges support by the European Research Council (2016-2021, Horizon 2020/ERC Grant No. 694409)
DS Minerva
RD 27 abr 2026