Single-Index Quantile Regression Models for Censored Data

Axel Bücher, Anouar El Ghouch and Ingrid Van Keilegom
Additional contact information
Anouar El Ghouch: Université catholique de Louvain, LIDAM/ISBA, Belgium
Ingrid Van Keilegom: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2021026, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: When the dimension of the covariate space is high, semiparametric regression models become indispensable to gain flexibility while avoiding the curse of dimensionality. These considerations become even more important for incomplete data. In this work, we consider the estimation of a semiparametric single-index model for conditional quantiles with right-censored data. Iteratively applying the local-linear smoothing approach, we simultaneously estimate the linear coefficients and the link function. We show that our estimating procedure is consistent and we study its asymptotic distribution. Numerical results are used to show the validity of our procedure and to illustrate the finite-sample performance of the proposed estimators.

Pages: 20
Date: 2021-06-15
Note: In: Daouia A., Ruiz-Gazen A. (eds), Advances in Contemporary Statistics and Econometrics, Springer, 2021, p. 177-196
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2021026

DOI: 10.1007/978-3-030-73249-3_10

Access Statistics for this paper

More papers in LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) Voie du Roman Pays 20, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Nadja Peiffer ().