Authors: | Chown, Justin Heuchenne, Cédric Van Keilegom, Ingrid |
Title: | The nonparametric location-scale mixture cure model |
Language (ISO): | en |
Abstract: | We propose completely nonparametric methodology to investigate location-scale modelling of two-component mixture cure models, where the responses of interest are only indirectly observable due to the presence of censoring and the presence of so-called long-term survivors that are always censored. We use covariate-localized nonparametric estimators, which 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 band-widths, the asymptotic distribution of the proposed estimator of the error distribution function does not depend on this bandwidth, and this estimator is shown to be root-n consistent. This suggests that a computationally costly bandwidth selection procedure is unnecessary to obtain an effective estimator of the error distribution, and that a simpler rule-of-thumb approach can be used instead. 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. |
Subject Headings: | censored data nonparametric regression error distribution function cure model |
URI: | http://hdl.handle.net/2003/36818 http://dx.doi.org/10.17877/DE290R-18819 |
Issue Date: | 2018 |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_0718_SFB823_Chown_Heuchenne_VanKeilegom.pdf | DNB | 466.01 kB | Adobe PDF | View/Open |
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