A lack-of-fit test for quantile regression process models. (English) Zbl 1503.62040
Summary: Quantile regression is a widely used statistical tool for data analysis in practice, but model misspecifications may lead to incorrect inferences. In this paper, a lack-of-fit test for quantile regression processes is proposed for those cases with multivariate covariates, which has not been well studied in the existing literature. An asymptotic result is established, and a numerical study has demonstrated that the proposed method is promising.
MSC:
| 62G08 | Nonparametric regression and quantile regression |
| 62G10 | Nonparametric hypothesis testing |
| 62G20 | Asymptotic properties of nonparametric inference |
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