Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106858
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | - |
dc.creator | Hao, M | - |
dc.creator | Lin, Y | - |
dc.creator | Shen, G | - |
dc.creator | Su, W | - |
dc.date.accessioned | 2024-06-06T06:05:19Z | - |
dc.date.available | 2024-06-06T06:05:19Z | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/10397/106858 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier BV | en_US |
dc.subject | Asymptotic normality | en_US |
dc.subject | Bahadur representation | en_US |
dc.subject | Quantile regression process | en_US |
dc.title | Nonparametric inference on smoothed quantile regression process | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 179 | - |
dc.identifier.doi | 10.1016/j.csda.2022.107645 | - |
dcterms.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. | - |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | Computational statistics and data analysis, Mar. 2023, v. 179, 107645 | - |
dcterms.isPartOf | Computational statistics and data analysis | - |
dcterms.issued | 2023-03 | - |
dc.identifier.scopus | 2-s2.0-85141671909 | - |
dc.identifier.eissn | 1872-7352 | - |
dc.identifier.artn | 107645 | - |
dc.description.validate | 202406 bcch | - |
dc.identifier.FolderNumber | a2752 | en_US |
dc.identifier.SubFormID | 48238 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The work of Y. Lin is partially supported by the Hong Kong Research Grants Council (Grant No. 14306219 and 14306620), the National Natural Science Foundation of China (Grant No. 11961028) and Direct Grants for Research, The Chinese University of Hong Kong; M. Hao's research is partly supported by the National Natural Science Foundation of China (No. 11901087) and the Program for Young Excellent Talents, UIBE (No. 19YQ15) | en_US |
dc.description.pubStatus | Published | en_US |
dc.date.embargo | 2025-03-31 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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