López-Cheda, AnaCao, RicardoJácome, M. A.Keilegom, Ingrid Van2026-02-112026-02-112025-11-11López-Cheda, A., Cao, R., Jácome, M.A., Van Keilegom, I. (2025). Nonparametric kernel estimation of cure models in survival analysis. Boletín de Estadística e Investigación Operativa. 41(3), 9-22. https://doi.org/10.63552/beio.2025.41.3.021889-3805https://hdl.handle.net/2183/47363[Abstract]: In lifetime data, there may be long-term survivors, which lead to heavy censoring at the end of the follow-up period. In this context, a standard survival model is not appropriate, and therefore a cure model is needed. In the literature, the covariate effect in mixture cure models was modeled using parametric or semiparametric approaches. Recently, completely nonparametric methods for the cure rate and the latency were proposed in mixture cure models. In this paper, we present this methodology and apply it to a colorectal cancer dataset.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Censored dataCure modelsKernel estimationLocal maximum likelihoodSurvival Analysisjournal articleopen access10.63552/beio.2025.41.3.02