The nonparametric location-scale mixture cure model

Justin Chown, Cédric Heuchenne and Ingrid Van Keilegom
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Cédric Heuchenne: Université catholique de Louvain, LIDAM/ISBA, Belgium
Ingrid Van Keilegom: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2021034, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: We propose completely nonparametric methodology to investigate location-scale modeling of two-component mixture cure models that is similar in spirit to accelerated failure time models, where the responses of interest are only indirectly observable due to the presence of censoring and the presence of long-term survivors that are always censored. We use nonparametric estimators of the location-scale model components that depend on a bandwidth sequence to propose an estimator of the error distribution function that has not been considered before in the literature. When this bandwidth belongs to a certain range of undersmoothing bandwidths, the proposed estimator of the error distribution function is root-n consistent. A simulation study investigates the finite sample properties of our approach, and the methodology is illustrated using data obtained to study the behavior of distant metastasis in lymph-node-negative breast cancer patients.

Keywords: Censored data; cure model; error distribution function; nonparametric regression (search for similar items in EconPapers)
Pages: 17
Date: 2021-01-01
Note: In: Test, 2020, vol. 29, p. 1008–1028
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2021034

DOI: 10.1007/s11749-019-00698-8

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