Nonparametric inference on smoothed quantile regression process

Meiling Hao, Yuanyuan Lin, Guohao Shen and Wen Su

Computational Statistics & Data Analysis, 2023, vol. 179, issue C

Abstract: This paper studies the global estimation in semiparametric quantile regression models. For estimating unknown functional parameters, an integrated quantile regression loss function with penalization is proposed. The first step is to obtain a vector-valued functional Bahadur representation of the resulting estimators, and then derive the asymptotic distribution of the proposed infinite-dimensional estimators. Furthermore, a resampling approach that generalizes the minimand perturbing technique is adopted to construct confidence intervals and to conduct hypothesis testing. Extensive simulation studies demonstrate the effectiveness of the proposed method, and applications to the real estate dataset and world happiness report data are provided.

Keywords: Asymptotic normality; Bahadur representation; Quantile regression process (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:179:y:2023:i:c:s0167947322002250

DOI: 10.1016/j.csda.2022.107645

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