Inference for High-Dimensional Censored Quantile Regression Article Swipe
Zhe Fei
,
Qi Zheng
,
Hyokyoung G. Hong
,
Yi Li
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2021.1957900
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2021.1957900
With the availability of high dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inference on the effects of high dimensional predictors for censored quantile regression. This paper proposes a novel procedure to draw inference on all predictors within the framework of global censored quantile regression, which investigates covariate-response associations over an interval of quantile levels, instead of a few discrete values. The proposed estimator combines a sequence of low dimensional model estimates that are based on multi-sample splittings and variable selection. We show that, under some regularity conditions, the estimator is consistent and asymptotically follows a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure can properly quantify the uncertainty of the estimates in high dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in lung cancer pathways on patients' survival, using the Boston Lung Cancer Survivor Cohort, a cancer epidemiology study on the molecular mechanism of lung cancer.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/01621459.2021.1957900
- OA Status
- green
- Cited By
- 13
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3193816867
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3193816867Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/01621459.2021.1957900Digital Object Identifier
- Title
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Inference for High-Dimensional Censored Quantile RegressionWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-07-21Full publication date if available
- Authors
-
Zhe Fei, Qi Zheng, Hyokyoung G. Hong, Yi LiList of authors in order
- Landing page
-
https://doi.org/10.1080/01621459.2021.1957900Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10259833/pdf/nihms-1735066.pdfDirect OA link when available
- Concepts
-
Quantile regression, Inference, Econometrics, Statistics, Quantile, Regression, Computer science, Mathematics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 6, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
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74Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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