Goodness-of-fit tests for quantile regression with missing responses

Ana Pérez-González (), Tomás R. Cotos-Yáñez, Wenceslao González-Manteiga () and Rosa M. Crujeiras-Casais ()
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Ana Pérez-González: University of Vigo
Tomás R. Cotos-Yáñez: University of Vigo
Wenceslao González-Manteiga: Universidade de Santiago de Compostela
Rosa M. Crujeiras-Casais: Universidade de Santiago de Compostela

Statistical Papers, 2021, vol. 62, issue 3, No 8, 1264 pages

Abstract: Abstract Goodness-of-fit tests for quantile regression models, in the presence of missing observations in the response variable, are introduced and analysed in this paper. The different proposals are based on the construction of empirical processes considering three different approaches which involve the use of the gradient vector of the quantile function, a linear projection of the covariates (suitable for high-dimensional settings) and a projection of the estimating equations. Besides, two types of estimators for the null parametric model to be tested are considered. The performance of the different test statistics is analysed in an extensive simulation study. An application to real data is also included.

Keywords: Goodness-of-fit test; Missing data; Quantile regression (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00362-019-01135-6

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