I am reading a paper which estimates the survival function with a nonparametric estimator (e.g. Kaplan-Meier) and then uses machine learning methods to estimate the complete survival function. This is compared with the "traditional two-parameter Weibull reliability estimation". The paper doesn't describe how the Weibull parameters are estimated and I couldn't find information about what is the "traditional method".
Also the error is compared with tenfold-crossvalidation. This is clear for the machine learning methods, but I I have no idea how you would do this with the traditional method. I calculated a survival function with the weibullfitter method from lifelines in python for one dataset that was provided and calculated the error with respect to the nonparametric estimation, but I got higher errors, so that's probably not what they are doing.
The paper is called "On the use of machine learning methods to predict component reliability from data-driven industrial case studies" from Alsina, Chica, Trawinski, Regattieri (doi: 10.1007/s00170-017-1039-x)
Can somebody help me with which method is used to estimate the Weibull parameters and how (or if you even can) calculate the error for the Weibull estimation? A link/book or somewthing where I can look this up would be sufficient.