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A lack-of-fit test for quantile regression process models

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  • Feng, Xingdong
  • Liu, Qiaochu
  • Wang, Caixing

Abstract

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.

Suggested Citation

  • Feng, Xingdong & Liu, Qiaochu & Wang, Caixing, 2023. "A lack-of-fit test for quantile regression process models," Statistics & Probability Letters, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:stapro:v:192:y:2023:i:c:s0167715222001936
    DOI: 10.1016/j.spl.2022.109680
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    References listed on IDEAS

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