López-Cheda, AnaCao, RicardoJácome, M. A.Keilegom, Ingrid Van2023-12-212023-12-212017-01A. López-Cheda, R. Cao, M. A. Jácome, y I. Van Keilegom, «Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models», Computational Statistics & Data Analysis, vol. 105, pp. 144-165, ene. 2017, doi: 10.1016/j.csda.2016.08.002.1872-7352http://hdl.handle.net/2183/34587© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article [López-Cheda, A., Cao, R., Jácome, M.A., Van Keilegom, I., 2017. Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models. Computational Statistics & Data Analysis 105, 144–165] has been accepted for publication in Computational Statistics & Data Analysis. The Version of Record is available online at https://doi.org/10.1016/j.csda.2016.08.002.[Abstract]: A completely nonparametric method for the estimation of mixture cure models is proposed. A nonparametric estimator of the incidence is extensively studied and a nonparametric estimator of the latency is presented. These estimators, which are based on the Beran estimator of the conditional survival function, are proved to be the local maximum likelihood estimators. An i.i.d. representation is obtained for the nonparametric incidence estimator. As a consequence, an asymptotically optimal bandwidth is found. Moreover, a bootstrap bandwidth selection method for the nonparametric incidence estimator is proposed. The introduced nonparametric estimators are compared with existing semiparametric approaches in a simulation study, in which the performance of the bootstrap bandwidth selector is also assessed. Finally, the method is applied to a database of colorectal cancer from the University Hospital of A Coruña (CHUAC).engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Survival analysisCensored dataLocal maximum likelihoodKernel estimationjournal articleopen access