Quantile Regression for Interval Censored Data using an Enriched Laplace Distribution

Benjamin Deketelaere and Ingrid Van Keilegom
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Benjamin Deketelaere: Université catholique de Louvain, LIDAM/ISBA, Belgium
Ingrid Van Keilegom: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2026001, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: Consider a linear quantile regression model in which the response is subject to interval censoring. Quantile regression is an alternative to the common mean regression, and has the advantage that it allows to give a broader view of the conditional distribution and it is less sensitive to outliers. We propose novel estimators of the quantile regression coefficients for any quantile level 0

Keywords: Bootstrap; consistency; cross-validation; interval censoring; Laguerre polynomials; quantile regression; survival analysis (search for similar items in EconPapers)
Pages: 32
Date: 2026-01-01
Note: In: Biometrical Journal, 2026
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2026001

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