Assessing model adequacy in possibly misspecified quantile regression
(2013) Computational Statistics & Data Analysis — Vol. 57, n° 1, p. 558-569 (2013)
Files
RP_2013_01_vankeilegom_Assessing_model.pdf
Open Access - Adobe PDF
- 826.32 KB
Details
- Authors
- Abstract
- Possibly misspecified linear quantile regression models are considered. A measure for assessing the combined effect of several covariates on a certain conditional quantile function is proposed. The measure is based on an adaptation to quantile regression of the famous coefficient of determination originally proposed for mean regression, and compares a ‘reduced’ model to a ‘full’ model, both of which can be misspecified. An estimator of this measure is proposed and its asymptotic distribution is investigated both in the nondegenerate and the degenerate case. The finite sample performance of the estimator is studied through a number of simulation experiments. The proposed measure is also applied to a data set on body fat measures.
- Affiliations
Citations
Noh, H., El Ghouch, A., & Van Keilegom, I. (2013). Assessing model adequacy in possibly misspecified quantile regression. Computational Statistics & Data Analysis, 57(1), 558-569. https://doi.org/10.1016/j.csda.2012.07.020 (Original work published 2013)