Assessing model adequacy in possibly misspecified quantile regression
- Authors
- Noh, H (Noh, Hohsuk); El Ghouch, A (El Ghouch, Anoua; Van Keilegom, I (Van Keilegom,
- Issue Date
- Jan-2013
- Publisher
- ELSEVIER SCIENCE BV
- Citation
- COMPUTATIONAL STATISTICS DATA ANALYSIS, v.57, no.1, pp 558 - 569
- Pages
- 12
- Journal Title
- COMPUTATIONAL STATISTICS DATA ANALYSIS
- Volume
- 57
- Number
- 1
- Start Page
- 558
- End Page
- 569
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/147507
- DOI
- 10.1016/j.csda.2012.07.020
- ISSN
- 0167-9473
- 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 non-degenerate 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. (C) 2012 Elsevier B.V. All rights reserved.
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